Running experiments with different machine learning models for a multi class music genre classification task.
import os
import cv2
import librosa
from IPython.display import Audio, display
from tqdm import tqdm
import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import RobustScaler
from sklearn.cluster import KMeans
from tensorflow import keras
from tensorflow.keras.utils import to_categorical
from tensorflow.keras import activations, layers, optimizers, losses
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras import Sequential
import keras_tuner as kt
from extra_keras_metrics import get_standard_binary_metrics
from sklearn.metrics import accuracy_score, average_precision_score, roc_auc_score
from sklearn.model_selection import StratifiedShuffleSplit, ShuffleSplit
from matplotlib import pyplot as plt
from plot_keras_history import plot_history
import plotly.express as px
sr=22050
def loadData(folder: str) -> dict:
""" Return different data features from the GTZAN dataset.
Audio files,
2 set of features of the audio files (on a 3s and 30s window),
visual representations of audio files: mfccs (3s window), spectrogram (3s window), mel spectrogram (3s and 30s window).
Parameters
------------------------
folden: str
The folder with the GTZAN dataset.
Returns
------------------------
dict
A dictionary with all the useful GTZAN features and labels."""
# Retrieve the features (30s window)
features_30s = pd.read_csv(os.path.join(folder, 'features_30_sec.csv'))
# Retrieve the features (3s window)
features_3s = pd.read_csv(os.path.join(folder, 'features_3_sec.csv'))
audio_folder = os.path.join(folder, 'genres_original')
images_folder = os.path.join(folder, 'images_original')
audio = []
mel_spectrogram_30s = []
mel_spectrogram_3s = []
spectrogram = []
frame_mfccs = []
frame_labels = []
labels = []
for genre in os.listdir(audio_folder):
# Retrieve audio files
for fileName in os.listdir(os.path.join(audio_folder, genre)):
audio_genre_path = os.path.join(audio_folder, genre)
audioFile,_ = librosa.load(os.path.join(audio_genre_path, fileName), sr=sr)
if audioFile is not None:
audio.append(audioFile)
labels.append(genre)
# Frame 3 seconds windows
audio_frames = librosa.util.frame(audioFile, frame_length=sr*3, hop_length=sr*3)
for i in range(audio_frames.shape[1]):
mfccs = librosa.feature.mfcc(y=audio_frames[:,i], sr=sr, n_mfcc=20)
frame_mfccs.append(mfccs)
frame_labels.append(genre)
# Spectrogram
STFTspectrogram = np.abs(librosa.stft(y=audio_frames[:,i]))
spectrogram.append(STFTspectrogram)
# Mel Spectrogram
melSpectrogram = librosa.feature.melspectrogram(y=audio_frames[:,i], sr=sr)
mel_spectrogram_3s.append(melSpectrogram)
# Retrieve images (mel spectrogram 30s)
for fileName in os.listdir(os.path.join(images_folder, genre)):
images_genre_path = os.path.join(images_folder, genre)
img = cv2.imread(os.path.join(images_genre_path, fileName))
if img is not None:
RGBImg = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
grayImg = cv2.cvtColor(RGBImg, cv2.COLOR_RGB2GRAY)
mel_spectrogram_30s.append(grayImg)
data = {
"features_30s": features_30s,
"features_3s": features_3s,
"audio": audio,
"mfccs": frame_mfccs,
"spectrogram": spectrogram,
"mel_spectrogram_3s": mel_spectrogram_3s,
"mel_spectrogram_30s": mel_spectrogram_30s,
"labels_3s": frame_labels,
"labels_30s": labels
}
return data
folder = 'D:\MachineLearning\Datasets\GTZAN'
data = loadData(folder)
# The audio file 'jazz.00054.wav' is known to be corrupted, the file and the 30s mel spectogram image were removed.
data['features_3s'].drop(data['features_3s'][data['features_3s']['filename'].str.startswith('jazz.00054')].index, inplace=True)
data['features_30s'].drop(data['features_30s'][data['features_30s']['filename'].str.startswith('jazz.00054')].index, inplace=True)
data['features_30s'].columns
Index(['filename', 'length', 'chroma_stft_mean', 'chroma_stft_var', 'rms_mean',
'rms_var', 'spectral_centroid_mean', 'spectral_centroid_var',
'spectral_bandwidth_mean', 'spectral_bandwidth_var', 'rolloff_mean',
'rolloff_var', 'zero_crossing_rate_mean', 'zero_crossing_rate_var',
'harmony_mean', 'harmony_var', 'perceptr_mean', 'perceptr_var', 'tempo',
'mfcc1_mean', 'mfcc1_var', 'mfcc2_mean', 'mfcc2_var', 'mfcc3_mean',
'mfcc3_var', 'mfcc4_mean', 'mfcc4_var', 'mfcc5_mean', 'mfcc5_var',
'mfcc6_mean', 'mfcc6_var', 'mfcc7_mean', 'mfcc7_var', 'mfcc8_mean',
'mfcc8_var', 'mfcc9_mean', 'mfcc9_var', 'mfcc10_mean', 'mfcc10_var',
'mfcc11_mean', 'mfcc11_var', 'mfcc12_mean', 'mfcc12_var', 'mfcc13_mean',
'mfcc13_var', 'mfcc14_mean', 'mfcc14_var', 'mfcc15_mean', 'mfcc15_var',
'mfcc16_mean', 'mfcc16_var', 'mfcc17_mean', 'mfcc17_var', 'mfcc18_mean',
'mfcc18_var', 'mfcc19_mean', 'mfcc19_var', 'mfcc20_mean', 'mfcc20_var',
'label'],
dtype='object')
# Drop useless columns
data['features_30s'].drop(columns='filename', inplace=True)
data['features_3s'].drop(columns='filename', inplace=True)
data['features_30s'].drop(columns='length', inplace=True)
data['features_3s'].drop(columns='length', inplace=True)
data['features_30s'].shape
(999, 58)
data['features_30s'].head()
| chroma_stft_mean | chroma_stft_var | rms_mean | rms_var | spectral_centroid_mean | spectral_centroid_var | spectral_bandwidth_mean | spectral_bandwidth_var | rolloff_mean | rolloff_var | ... | mfcc16_var | mfcc17_mean | mfcc17_var | mfcc18_mean | mfcc18_var | mfcc19_mean | mfcc19_var | mfcc20_mean | mfcc20_var | label | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.350088 | 0.088757 | 0.130228 | 0.002827 | 1784.165850 | 129774.064525 | 2002.449060 | 85882.761315 | 3805.839606 | 9.015054e+05 | ... | 52.420910 | -1.690215 | 36.524071 | -0.408979 | 41.597103 | -2.303523 | 55.062923 | 1.221291 | 46.936035 | blues |
| 1 | 0.340914 | 0.094980 | 0.095948 | 0.002373 | 1530.176679 | 375850.073649 | 2039.036516 | 213843.755497 | 3550.522098 | 2.977893e+06 | ... | 55.356403 | -0.731125 | 60.314529 | 0.295073 | 48.120598 | -0.283518 | 51.106190 | 0.531217 | 45.786282 | blues |
| 2 | 0.363637 | 0.085275 | 0.175570 | 0.002746 | 1552.811865 | 156467.643368 | 1747.702312 | 76254.192257 | 3042.260232 | 7.840345e+05 | ... | 40.598766 | -7.729093 | 47.639427 | -1.816407 | 52.382141 | -3.439720 | 46.639660 | -2.231258 | 30.573025 | blues |
| 3 | 0.404785 | 0.093999 | 0.141093 | 0.006346 | 1070.106615 | 184355.942417 | 1596.412872 | 166441.494769 | 2184.745799 | 1.493194e+06 | ... | 44.427753 | -3.319597 | 50.206673 | 0.636965 | 37.319130 | -0.619121 | 37.259739 | -3.407448 | 31.949339 | blues |
| 4 | 0.308526 | 0.087841 | 0.091529 | 0.002303 | 1835.004266 | 343399.939274 | 1748.172116 | 88445.209036 | 3579.757627 | 1.572978e+06 | ... | 86.099236 | -5.454034 | 75.269707 | -0.916874 | 53.613918 | -4.404827 | 62.910812 | -11.703234 | 55.195160 | blues |
5 rows × 58 columns
data['features_3s'].shape
(9980, 58)
data['features_3s'].head()
| chroma_stft_mean | chroma_stft_var | rms_mean | rms_var | spectral_centroid_mean | spectral_centroid_var | spectral_bandwidth_mean | spectral_bandwidth_var | rolloff_mean | rolloff_var | ... | mfcc16_var | mfcc17_mean | mfcc17_var | mfcc18_mean | mfcc18_var | mfcc19_mean | mfcc19_var | mfcc20_mean | mfcc20_var | label | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.335406 | 0.091048 | 0.130405 | 0.003521 | 1773.065032 | 167541.630869 | 1972.744388 | 117335.771563 | 3714.560359 | 1.080790e+06 | ... | 39.687145 | -3.241280 | 36.488243 | 0.722209 | 38.099152 | -5.050335 | 33.618073 | -0.243027 | 43.771767 | blues |
| 1 | 0.343065 | 0.086147 | 0.112699 | 0.001450 | 1816.693777 | 90525.690866 | 2010.051501 | 65671.875673 | 3869.682242 | 6.722448e+05 | ... | 64.748276 | -6.055294 | 40.677654 | 0.159015 | 51.264091 | -2.837699 | 97.030830 | 5.784063 | 59.943081 | blues |
| 2 | 0.346815 | 0.092243 | 0.132003 | 0.004620 | 1788.539719 | 111407.437613 | 2084.565132 | 75124.921716 | 3997.639160 | 7.907127e+05 | ... | 67.336563 | -1.768610 | 28.348579 | 2.378768 | 45.717648 | -1.938424 | 53.050835 | 2.517375 | 33.105122 | blues |
| 3 | 0.363639 | 0.086856 | 0.132565 | 0.002448 | 1655.289045 | 111952.284517 | 1960.039988 | 82913.639269 | 3568.300218 | 9.216524e+05 | ... | 47.739452 | -3.841155 | 28.337118 | 1.218588 | 34.770935 | -3.580352 | 50.836224 | 3.630866 | 32.023678 | blues |
| 4 | 0.335579 | 0.088129 | 0.143289 | 0.001701 | 1630.656199 | 79667.267654 | 1948.503884 | 60204.020268 | 3469.992864 | 6.102111e+05 | ... | 30.336359 | 0.664582 | 45.880913 | 1.689446 | 51.363583 | -3.392489 | 26.738789 | 0.536961 | 29.146694 | blues |
5 rows × 58 columns
sample = 196
print(data['features_30s']['label'][sample])
plt.imshow(data['mel_spectrogram_30s'][sample], cmap='gray', vmin=0, vmax=255)
audioSample = data['audio'][sample]
Audio(data=audioSample, rate=sr)
classical
If the number of features is higher than the number of samples the model will behave erratically and overfit.
print(f"30s samples-features ratio: {data.get('features_30s').shape[0]/data.get('features_30s').shape[1]}")
print(f"3s samples-features ratio: {data.get('features_3s').shape[0]/data.get('features_3s').shape[1]}")
30s samples-features ratio: 17.224137931034484 3s samples-features ratio: 172.06896551724137
NaN values have a detrimental impact on the performance of the model.
print("NaN values report for 30s features: ")
print(f"Total NaN values: {data['features_30s'].isna().values.sum()} out of {data['features_30s'].values.size} values")
print("-"*85)
print("NaN values report for 3s features: ")
print(f"Total NaN values: {data['features_3s'].isna().values.sum()} out of {data['features_3s'].values.size} values")
NaN values report for 30s features: Total NaN values: 0 out of 57942 values ------------------------------------------------------------------------------------- NaN values report for 3s features: Total NaN values: 0 out of 578840 values
le = LabelEncoder()
labels_30s = pd.DataFrame(le.fit_transform(data['features_30s']['label']))
print(labels_30s.sample(3))
data['features_30s'].drop(columns='label', inplace=True)
print("-"*40)
labels_3s = pd.DataFrame(le.fit_transform(data['features_3s']['label']))
print(labels_3s.sample(3))
data['features_3s'].drop(columns='label', inplace=True)
print("-"*40)
data['labels_30s'] = pd.DataFrame(le.fit_transform(data['labels_30s']))
print(data['labels_30s'].sample(3))
print("-"*40)
data['labels_3s'] = pd.DataFrame(le.fit_transform(data['labels_3s']))
print(data['labels_3s'].sample(3))
0
920 9
943 9
972 9
----------------------------------------
0
4928 4
8186 8
6813 6
----------------------------------------
0
874 8
544 5
87 0
----------------------------------------
0
1759 1
7696 7
4915 4
audioSample.shape
(661794,)
plt.figure()
librosa.display.waveshow(audioSample)
plt.xlabel('Time (s)')
plt.ylabel('Amplitude')
plt.show()
data['spectrogram'][sample].shape
(1025, 130)
librosa.display.specshow(data['spectrogram'][sample], sr=sr, x_axis='time')
<matplotlib.collections.QuadMesh at 0x2062955bdf0>
data['mel_spectrogram_3s'][sample].shape
(128, 130)
librosa.display.specshow(data['mel_spectrogram_3s'][sample], sr=sr, x_axis='time')
<matplotlib.collections.QuadMesh at 0x20633b81070>
data['mfccs'][sample].shape
(20, 130)
librosa.display.specshow(data['mfccs'][sample], sr=sr, x_axis='time')
<matplotlib.collections.QuadMesh at 0x20627dab130>
Visualizing the data helps to evaluate the difficulty of the task and to design models with the appropiate complexity.
First, a parallel plot showing the value distribution of the main features with respect to the label to determine wheter these features are meaningful for the classification task, and to what extent.
# Main features mean (similar results when considering variance)
features_mean = ['chroma_stft_mean', 'rms_mean', 'spectral_centroid_mean', 'spectral_bandwidth_mean',
'rolloff_mean', 'zero_crossing_rate_mean', 'harmony_mean', 'perceptr_mean']
fig = px.parallel_coordinates(data['features_30s'], color=np.array(labels_30s).ravel(),
dimensions=features_mean,
color_continuous_midpoint=5)
fig.show()
# Mfccs mean (similar results when considering variance)
mfccs_mean = ['mfcc1_mean', 'mfcc2_mean', 'mfcc3_mean', 'mfcc4_mean', 'mfcc4_mean', 'mfcc5_mean',
'mfcc6_mean', 'mfcc7_mean', 'mfcc8_mean', 'mfcc9_mean', 'mfcc10_mean',
'mfcc11_mean', 'mfcc12_mean', 'mfcc13_mean']
fig = px.parallel_coordinates(data['features_30s'], color=np.array(labels_30s).ravel(),
dimensions=mfccs_mean,
color_continuous_midpoint=5)
fig.show()
Next the PCA data decomposition technique is used to reduce the high dimensional dataset into fewer dimensions (two), while preserving spatial characteristics for visualization purposes.
Technique to reduce the dimensionality of a dataset by linearly transforming the data into a new coordinate system where the variation in the data can be described with fewer dimensions than the initial data while preserving most of the original data variation.
The following is a visualization of the feature space considering the features apart from mfccs as well as mfccs only.
The simple clustering algorithm K-means is also applied to evaluate the task difficulty and the feature space.
from sklearn.decomposition import PCA
def get_pca_decomposition(
X: pd.DataFrame
) -> pd.DataFrame:
"""Return the 2D PCA decomposition of the given data."""
return pd.DataFrame(
PCA(n_components=2).fit_transform(X.values),
index=X.index
)
def reshape(data,shape):
"""Return the data reshaped."""
data = data.reshape(shape)
#data = np.expand_dims(data,axis=-1)
return data
decomposed_features = np.array(get_pca_decomposition(data['features_3s'][features_mean]))
print(f"PCA decomposition: 3s window features data shape: {decomposed_features.shape}")
reshaped_mfccs = [reshape(x, (2600,1)) for x in data['mfccs']]
reshaped_mfccs = np.squeeze(np.array(reshaped_mfccs))
decomposed_mfccs = np.array(get_pca_decomposition(pd.DataFrame(reshaped_mfccs)))
print(f"PCA decomposition: mfccs data shape: {decomposed_mfccs.shape}")
PCA decomposition: 3s window features data shape: (9980, 2) PCA decomposition: mfccs data shape: (9981, 2)
kMeans_features = KMeans(n_clusters = 10, random_state = 42).fit(decomposed_features)
kMeans_mfccs = KMeans(n_clusters = 10, random_state = 42).fit(decomposed_mfccs)
predictions_features = kMeans_features.fit_predict(decomposed_features)
predictions_mfccs = kMeans_mfccs.fit_predict(decomposed_mfccs)
labels = np.unique(labels_3s)
for decomposed_data, ground_truth, predictions in ((decomposed_features, labels_3s, predictions_features), (decomposed_mfccs, data['labels_3s'], predictions_mfccs)):
if np.array_equal(decomposed_data, decomposed_features):
print("- 3s window features visualization -")
else:
print("- 3s window mfccs visualization -")
plt.figure(figsize=(12,6))
for l in labels:
plt.scatter(decomposed_data[np.squeeze(ground_truth == l), 0], decomposed_data[np.squeeze(ground_truth == l), 1], label = l, marker = ".")
plt.show()
print("- K-means predictions -")
plt.figure(figsize=(12,6))
for l in labels:
plt.scatter(decomposed_data[predictions == l, 0], decomposed_data[predictions == l, 1], label = l, marker =".")
plt.show()
print("-"*100)
- 3s window features visualization -
- K-means predictions -
---------------------------------------------------------------------------------------------------- - 3s window mfccs visualization -
- K-means predictions -
----------------------------------------------------------------------------------------------------
from sklearn.metrics import mutual_info_score
from sklearn.metrics import adjusted_rand_score
from sklearn.metrics import mean_squared_error
from sklearn.metrics import silhouette_score
print("K-means performance on features a part from mfccs:")
mif_features = mutual_info_score(np.squeeze(np.array(labels_3s)), predictions_features)
print(f"Mutual info score: {mif_features}")
ars_features = adjusted_rand_score(np.squeeze(np.array(labels_3s)), predictions_features)
print(f"Adjusted rand score: {ars_features}")
silhouette_features = silhouette_score(np.array(labels_3s), predictions_features)
print(f"Silhouette score: {silhouette_features}")
MSE_features = mean_squared_error(np.squeeze(np.array(labels_3s)), predictions_features)
print(f"Mean squared error: {MSE_features}")
print("-"*40)
print("K-means performance on mfccs:")
mif_mfccs = mutual_info_score(np.squeeze(np.array(data['labels_3s'])), predictions_mfccs)
print(f"Mutual info score: {mif_mfccs}")
ars_mfccs = adjusted_rand_score(np.squeeze(np.array(data['labels_3s'])), predictions_mfccs)
print(f"Adjusted rand score: {ars_mfccs}")
silhouette_mfccs = silhouette_score(data['labels_3s'], predictions_mfccs)
print(f"Silhouette score: {silhouette_mfccs}")
MSE_mfccs = mean_squared_error(np.squeeze(np.array(data['labels_3s'])), predictions_mfccs)
print(f"Mean squared error: {MSE_mfccs}")
K-means performance on features a part from mfccs: Mutual info score: 0.36924631327385926 Adjusted rand score: 0.06948465229335508 Silhouette score: -0.3345990353688898 Mean squared error: 18.009819639278557 ---------------------------------------- K-means performance on mfccs: Mutual info score: 0.4850546245740776 Adjusted rand score: 0.10482089465103901 Silhouette score: -0.308158151738995 Mean squared error: 15.191764352269312
The algorithm gives a numerical estimate of the feature importance and categorizes features in those to keep, those to discard and tentative, meaning that there is uncertainty regarding their actual correlation with the output. In this case it's used to evaluate features and determine if some are not useful for the task at hand.
from boruta import BorutaPy
from sklearn.ensemble import RandomForestClassifier
from multiprocessing import cpu_count
def boruta_feature_selection(
data: pd.DataFrame,
labels: pd.DataFrame,
max_iter: int = 100
) -> pd.DataFrame:
""" Return the features that the Boruta algorithm found to be correlated with the output (useful/to keep).
Parameters
------------------------
data: pd.DataFrame
The data reserved for the input of the training of the Boruta model.
labels: pd.DataFrame
The data reserved for the output of the training of the Boruta model.
max_iter: int = 100
The number of iterations to run Boruta for.
Returns
------------------------
list: list
The list of correlated features."""
# Create the Boruta model
boruta_selector = BorutaPy(
RandomForestClassifier(n_jobs=cpu_count(), class_weight='balanced', max_depth=5),
n_estimators = 'auto',
verbose = 2,
alpha = 0.05,
max_iter = max_iter,
random_state = 42
)
# Fit the Boruta model
boruta_selector.fit(data.values, labels.values.ravel())
# Get the kept features and discarded features
kept_features = list(data.columns[boruta_selector.support_])
discarted_features = list(data.columns[~boruta_selector.support_])
print(f"-- {len(kept_features)} Kept (correlated) features [Boruta]")
print(f"-- {len(discarted_features)} Discarted (uncorrelated) features [Boruta]")
return kept_features
useful = boruta_feature_selection(data['features_3s'], pd.DataFrame(labels_3s))
print("-"*50)
if (useful == list(data['features_3s'].columns)):
print(f"The Boruta algorithm found all features to be useful for the task")
else:
print(f"The Boruta algorithm found some features to be useless for the task")
Iteration: 1 / 100 Confirmed: 0 Tentative: 57 Rejected: 0 Iteration: 2 / 100 Confirmed: 0 Tentative: 57 Rejected: 0 Iteration: 3 / 100 Confirmed: 0 Tentative: 57 Rejected: 0 Iteration: 4 / 100 Confirmed: 0 Tentative: 57 Rejected: 0 Iteration: 5 / 100 Confirmed: 0 Tentative: 57 Rejected: 0 Iteration: 6 / 100 Confirmed: 0 Tentative: 57 Rejected: 0 Iteration: 7 / 100 Confirmed: 0 Tentative: 57 Rejected: 0 Iteration: 8 / 100 Confirmed: 57 Tentative: 0 Rejected: 0 BorutaPy finished running. Iteration: 9 / 100 Confirmed: 57 Tentative: 0 Rejected: 0 -- 57 Kept (correlated) features [Boruta] -- 0 Discarted (uncorrelated) features [Boruta] -------------------------------------------------- The Boruta algorithm found all features to be useful for the task
Data normalization is proven to improve the performance of models.
The robust scaler operates normalization subtracting the median and dividing by the standard deviation between the interquantile range. This is operates a z-scoring while being robust to outliers.
def robust_scaler(df: pd.DataFrame) -> pd.DataFrame:
""" Return the provided dataframe normalized using robust z-scoring.
Parameters
------------------------
df: pd.DataFrame
The dataframe to normalize.
Returns
------------------------
pd.DataFrame
The dataframe normalized."""
return pd.DataFrame(
RobustScaler().fit_transform(df.values),
columns=df.columns,
index=df.index
)
scaled_features_30s = robust_scaler(data['features_30s'])
scaled_features_3s = robust_scaler(data['features_3s'])
data['features_30s'] = scaled_features_30s
data['features_3s'] = scaled_features_3s
data['features_30s'].describe()
| chroma_stft_mean | chroma_stft_var | rms_mean | rms_var | spectral_centroid_mean | spectral_centroid_var | spectral_bandwidth_mean | spectral_bandwidth_var | rolloff_mean | rolloff_var | ... | mfcc16_mean | mfcc16_var | mfcc17_mean | mfcc17_var | mfcc18_mean | mfcc18_var | mfcc19_mean | mfcc19_var | mfcc20_mean | mfcc20_var | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 999.000000 | 999.000000 | 999.000000 | 999.000000 | 999.000000 | 999.000000 | 999.000000 | 999.000000 | 999.000000 | 999.000000 | ... | 999.000000 | 999.000000 | 999.000000 | 999.000000 | 999.000000 | 999.000000 | 999.000000 | 999.000000 | 999.000000 | 999.000000 |
| mean | -0.037851 | -0.031300 | 0.097015 | 0.469553 | -0.006729 | 0.305898 | 0.031691 | 0.218046 | -0.039926 | 0.206666 | ... | -0.010478 | 0.268226 | 0.014922 | 0.231580 | -0.031134 | 0.266441 | 0.013267 | 0.238869 | 0.014208 | 0.255608 |
| std | 0.702300 | 0.863431 | 0.737525 | 1.378834 | 0.672089 | 0.935415 | 0.784113 | 0.839954 | 0.731226 | 0.798173 | ... | 0.735674 | 1.077925 | 0.714467 | 0.978579 | 0.753685 | 1.025252 | 0.778901 | 1.007156 | 0.819708 | 1.054486 |
| min | -1.816241 | -4.693609 | -1.312989 | -0.685980 | -1.538638 | -0.771683 | -1.970585 | -0.880878 | -1.814857 | -0.817495 | ... | -2.715621 | -1.376913 | -2.067992 | -1.191448 | -2.460066 | -1.172136 | -3.339178 | -1.189867 | -4.007472 | -1.192744 |
| 25% | -0.545768 | -0.482841 | -0.400065 | -0.330872 | -0.547217 | -0.360159 | -0.467832 | -0.388631 | -0.593617 | -0.393220 | ... | -0.494484 | -0.382358 | -0.494247 | -0.405575 | -0.522549 | -0.383866 | -0.472161 | -0.425662 | -0.471549 | -0.391029 |
| 50% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 75% | 0.454232 | 0.517159 | 0.599935 | 0.669128 | 0.452783 | 0.639841 | 0.532168 | 0.611369 | 0.406383 | 0.606780 | ... | 0.505516 | 0.617642 | 0.505753 | 0.594425 | 0.477451 | 0.616134 | 0.527839 | 0.574338 | 0.528451 | 0.608971 |
| max | 2.411676 | 2.397530 | 3.094722 | 9.808043 | 2.088906 | 6.293162 | 1.918358 | 5.073482 | 1.865680 | 4.022762 | ... | 1.966018 | 10.863628 | 2.439651 | 10.264686 | 2.866132 | 8.284885 | 3.541748 | 9.092044 | 3.529839 | 10.417365 |
8 rows × 57 columns
Feature selection implies the choice of a subset of the original pool of features.
This is a useful step as irrelevant or partially relevant features can negatively impact the model performance, hence feature selection reduces noise, improves model accuracy and reduces training time by cutting the total number of features.
Features not correlated with the output do not carry relevan information for the model to exploit for the task at hand. These can be removed.
In the following section functions are defined and ran for each of these tests over the entire dataset. This is done here just for explanatory purposes, in fact the resulting correlated features are not dropped from the dataset as this would introduce bias due to data leakage. Instead these tests are saved for the training "main loop" and ran, for each holdout, over the training portion of the data only, maintaining test data unseen and reserving it for actual testing.
from scipy.stats import pearsonr
from scipy.stats import spearmanr
from minepy import MINE
from scipy.stats import entropy
p_value_threshold = 0.01
correlation_threshold = 0.05
def get_pearson_uncorrelated(
data: pd.DataFrame,
labels: pd.DataFrame,
p_value_threshold: float = 0.01
) -> list:
""" Return the list of features that the Pearson correlation test found to be uncorrelated with the labels.
Parameters
------------------------
data: pd.DataFrame
The dataset.
labels: pd.dataFrame
The data labels.
p_value_threshold: float = 0.01
The minimum p-value required to consider a feature uncorrelated.
Returns
------------------------
list: list
The list of uncorrelated features."""
uncorrelated_features = []
for feature in (data.columns):
correlation, p_value = pearsonr(data[feature].values.ravel(), labels.values.ravel())
if p_value > p_value_threshold:
if feature not in uncorrelated_features:
uncorrelated_features.append(feature)
return uncorrelated_features
print("-- Just for explanatory purpose --")
uncorrelated_features_pearson_30s = get_pearson_uncorrelated(data['features_30s'], labels_30s)
uncorrelated_features_pearson_3s = get_pearson_uncorrelated(data['features_3s'], labels_3s)
print(f"30s window: features not correlated with the output: [Pearson]\n{uncorrelated_features_pearson_30s}")
print("-"*80)
print(f"3s window: features not correlated with the output: [Pearson]\n{uncorrelated_features_pearson_3s}")
-- Just for explanatory purpose -- 30s window: features not correlated with the output: [Pearson] ['harmony_mean', 'perceptr_mean', 'tempo', 'mfcc1_var', 'mfcc2_var', 'mfcc3_mean', 'mfcc3_var', 'mfcc4_mean', 'mfcc5_mean', 'mfcc5_var', 'mfcc6_mean', 'mfcc8_var', 'mfcc9_var', 'mfcc10_var', 'mfcc13_mean', 'mfcc15_mean', 'mfcc16_mean', 'mfcc17_mean', 'mfcc19_mean'] -------------------------------------------------------------------------------- 3s window: features not correlated with the output: [Pearson] ['tempo', 'mfcc10_var', 'mfcc17_mean', 'mfcc19_mean']
def get_spearman_uncorrelated(
data: pd.DataFrame,
labels: pd.DataFrame,
p_value_threshold: float = 0.01
) -> list:
""" Return the list of features that the Spearman correlation test found to be uncorrelated with the labels.
Parameters
------------------------
data: pd.DataFrame
The dataset.
labels: pd.dataFrame
The data labels.
p_value_threshold: float = 0.01
The minimum p-value required to consider a feature uncorrelated.
Returns
------------------------
list: list
The list of uncorrelated features."""
uncorrelated_features = []
for feature in (data.columns):
correlation, p_value = spearmanr(data[feature].values.ravel(), labels.values.ravel())
if p_value > p_value_threshold:
if feature not in uncorrelated_features:
uncorrelated_features.append(feature)
return uncorrelated_features
print("-- Just for explanatory purpose --")
uncorrelated_features_spearman_30s = get_spearman_uncorrelated(data['features_30s'], labels_30s)
uncorrelated_features_spearman_3s = get_spearman_uncorrelated(data['features_3s'], labels_3s)
print(f"30s window: features not correlated with the output: [Spearman]\n{uncorrelated_features_spearman_30s}")
print("-"*80)
print(f"3s window: features not correlated with the output: [Spearman]\n{uncorrelated_features_spearman_3s}")
-- Just for explanatory purpose -- 30s window: features not correlated with the output: [Spearman] ['chroma_stft_var', 'harmony_mean', 'perceptr_mean', 'tempo', 'mfcc1_var', 'mfcc2_var', 'mfcc3_mean', 'mfcc4_mean', 'mfcc5_mean', 'mfcc5_var', 'mfcc7_var', 'mfcc8_var', 'mfcc9_var', 'mfcc10_var', 'mfcc13_mean', 'mfcc14_mean', 'mfcc15_mean', 'mfcc16_mean', 'mfcc17_mean', 'mfcc19_mean'] -------------------------------------------------------------------------------- 3s window: features not correlated with the output: [Spearman] ['tempo', 'mfcc16_mean', 'mfcc17_mean', 'mfcc19_mean']
print("-- Just for explanatory purpose --")
uncorrelated_features_30s = uncorrelated_features_pearson_30s + uncorrelated_features_spearman_30s
uncorrelated_features_3s = uncorrelated_features_pearson_3s + uncorrelated_features_spearman_3s
uncorrelated_features_30s = list(set(uncorrelated_features_30s))
uncorrelated_features_3s = list(set(uncorrelated_features_3s))
print(f"30s window: currently there are {len(uncorrelated_features_30s)} uncorrelated features with the output:\n{uncorrelated_features_30s}" )
print("-"*80)
print(f"3s window: currently there are {len(uncorrelated_features_3s)} uncorrelated features with the output:\n{uncorrelated_features_3s}" )
-- Just for explanatory purpose -- 30s window: currently there are 22 uncorrelated features with the output: ['mfcc13_mean', 'perceptr_mean', 'mfcc14_mean', 'mfcc5_var', 'mfcc16_mean', 'mfcc15_mean', 'mfcc2_var', 'mfcc7_var', 'chroma_stft_var', 'mfcc5_mean', 'mfcc4_mean', 'mfcc3_var', 'mfcc9_var', 'tempo', 'mfcc19_mean', 'harmony_mean', 'mfcc10_var', 'mfcc3_mean', 'mfcc17_mean', 'mfcc8_var', 'mfcc1_var', 'mfcc6_mean'] -------------------------------------------------------------------------------- 3s window: currently there are 5 uncorrelated features with the output: ['mfcc10_var', 'mfcc17_mean', 'mfcc16_mean', 'tempo', 'mfcc19_mean']
def get_mic_uncorrelated(
data: pd.DataFrame,
labels: pd.DataFrame,
features_to_check: list,
correlation_threshold: float = 0.05
) -> list:
""" Return the list of features that the MIC correlation test confirmed to be uncorrelated with the labels.
Parameters
------------------------
data: pd.DataFrame
The dataset.
labels: pd.dataFrame
The data labels.
features_to_check: list
The list of features to check with the MIC correlation test.
correlation_threshold: float = 0.05
The maximum correlation value required to consider a feature uncorrelated.
Returns
------------------------
list: list
The list of uncorrelated features."""
uncorrelated_features = []
for feature in (features_to_check):
mine = MINE()
mine.compute_score(data[feature].values.ravel(), labels.values.ravel())
score = mine.mic()
if score < correlation_threshold:
uncorrelated_features.append(feature)
return uncorrelated_features
print("-- Just for explanatory purpose --")
uncorrelated_features_mic_30s = get_mic_uncorrelated(data['features_30s'], labels_30s, uncorrelated_features_30s)
uncorrelated_features_mic_3s = get_mic_uncorrelated(data['features_3s'], labels_3s, uncorrelated_features_3s)
print(f"30s window: features not correlated with the output: [Confirmed via MIC]\n{uncorrelated_features_mic_30s}")
print("-"*80)
print(f"3s window: features not correlated with the output: [Confirmed via MIC]\n{uncorrelated_features_mic_3s}")
-- Just for explanatory purpose -- 30s window: features not correlated with the output: [Confirmed via MIC] [] -------------------------------------------------------------------------------- 3s window: features not correlated with the output: [Confirmed via MIC] []
def uncorrelated_features_test(
data: pd.DataFrame,
labels: pd.DataFrame,
p_value_threshold: float = 0.01,
correlation_threshold: float = 0.05
) -> list:
""" Return the list of features that the Pearson+Spearman+Mic correlation tests found to be uncorrelated with the output.
Parameters
------------------------
data: pd.DataFrame
The dataset.
labels: pd.dataFrame
The data labels.
p_value_threshold: float = 0.01
The minimum p-value required to consider a feature uncorrelated.
correlation_threshold: float = 0.05
The maximum correlation value required to consider a feature uncorrelated.
Returns
------------------------
list: list
The list of uncorrelated features."""
uncorrelated_features = []
#Pearson
uncorrelated_features += get_pearson_uncorrelated(data, labels, p_value_threshold)
#Spearman
uncorrelated_features += get_spearman_uncorrelated(data, labels, p_value_threshold)
uncorrelated_features = list(set(uncorrelated_features))
print(f"-- {len(uncorrelated_features)} Uncorrelated features: [Pearson+Spearman]\n{uncorrelated_features}")
actually_uncorrelated_features = uncorrelated_features
#Mic
actually_uncorrelated_features = get_mic_uncorrelated(data, labels, uncorrelated_features, correlation_threshold)
print(f"-- Actually uncorrelated features: [confirmed via MIC]\n{actually_uncorrelated_features}")
return actually_uncorrelated_features
Features correlated with each other carry the same kind of information. These can be removed.
Identifies linear correlation between features.
def correlated_features_test(
data: pd.DataFrame,
p_value_threshold: float = 0.01,
correlation_threshold: float = 0.97
) -> list:
""" Return the features that the Pearson correlation tests found to be correlated with other features.
Parameters
------------------------
data: pd.DataFrame
The dataset.
p_value_threshold: float = 0.01
The maximum p-value required to consider a feature correlated.
correlation_threshold: float = 0.97
The minimum correlation value required to consider a feature correlated.
Returns
------------------------
list: list
The list of correlated features."""
correlated_features = []
#Pearson
for i, feature1 in (enumerate(data.columns)):
for feature2 in data.columns[i+1:]:
correlation, p_value = pearsonr(data[feature1].values.ravel(), data[feature2].values.ravel())
correlation = np.abs(correlation)
if p_value < p_value_threshold and correlation > correlation_threshold:
if entropy(data[feature1]) > entropy(data[feature2]):
correlated_features.append(feature2)
else:
correlated_features.append(feature1)
correlated_features = list(set(correlated_features))
print(f"-- {len(correlated_features)} Correlated features: [Pearson]\n{correlated_features}")
return correlated_features
print("-- Just for explanatory purpose --")
correlated_features_30s = correlated_features_test(data['features_30s'])
correlated_features_3s = correlated_features_test(data['features_3s'])
-- Just for explanatory purpose -- -- 1 Correlated features: [Pearson] ['spectral_centroid_mean'] -- 1 Correlated features: [Pearson] ['spectral_centroid_mean']
def one_hot_encoding(
labels: pd.DataFrame,
num_classes: int
) -> pd.DataFrame:
""" Return the labels encoded with one-hot encoding.
Parameters
------------------------
labels: pd.DataFrame
The labels to be one-hot encoded.
num_classes: int
The number of classes.
Returns
------------------------
pd.DataFrame: pd.DataFrame
The one-hot encoded labels."""
return pd.DataFrame(to_categorical(labels, num_classes))
print("-- Just for explanatory purpose --")
encoded_features_30s = one_hot_encoding(labels_30s, 10)
encoded_features_30s.sample(5)
-- Just for explanatory purpose --
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 58 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 919 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 874 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
| 593 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 81 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
The technique used to generate the holdouts for the evaluation of the various architectures is the stratified Monte-Carlo method, producing each time a different arrangement of the training, validation and testing set, while keeping roughly the same class balance.
10 holdouts are considered to train and evaluate each model, 20% of the whole dataset is used for testing, 80% for training where 20% of it is reserved for the validation of the meta-model.
To avoid increasing excessively the computational complexity of the main training loop, the hyperparameter tuning step is performed, for all holdouts, considering a single split of the training set in a training and validation portion.
number_of_splits = 10
holdouts_generator = StratifiedShuffleSplit(
n_splits = number_of_splits,
test_size = 0.2,
random_state = 42
)
number_of_splits_tuning = 1
holdouts_generator_tuning = StratifiedShuffleSplit(
n_splits = number_of_splits_tuning,
test_size = 0.2,
random_state = 42
)
def train_model(
model: tf.keras.Model,
x_train: np.ndarray,
x_test: np.ndarray,
y_train: np.ndarray,
y_test: np.ndarray,
epochs: int,
batch_size: int
):
""" Train the model and return the performance metrics (accuracy, AUROC, AUPRC) for train and test and the training history.
Parameters
------------------------
model: tf.keras.Model
The model to train.
x_train: np.ndarray
The input data for training.
x_test: np.ndarray
The input data for testing.
y_train: np.ndarray
The labels of the input data for training.
y_test: np.ndarray
The labels of the input data for testing.
epochs: int
The number of times the learning algorithm works through the dataset.
batch_size: int
The number of samples to work through before updating the model.
Returns
------------------------
The performance metrics and history of the model."""
model_history = model.fit(
x_train, y_train,
validation_data = (x_test, y_test),
epochs = epochs,
batch_size = batch_size,
callbacks = [EarlyStopping("val_loss", patience=2)]
)
train_evaluation = model.evaluate(x_train, y_train, return_dict=True)
test_evaluation = model.evaluate(x_test, y_test, return_dict=True)
metrics = {"train_evaluation": train_evaluation, "test_evaluation": test_evaluation}
return metrics, model_history
Next are a set of functions to evaluate and visualize the performances of models.
To have a statistically sound estimate of an architecture performance, multiple models are built and trained, each with the same architecture, over different portions of the data (holdouts) and, the average performance of those, is considered as an estimate of the overall performance of the architecture.
def model_metrics_holdout_estimate(
model_metrics: list,
holdout_total_number: int
) -> dict[str, float]:
""" Return the average metrics (accuracy, AUROC, AUPRC) of the model accross all the holdouts for train and test.
Parameters
------------------------
model_metrics: list
The list with the train/test metrics (accuracy, AUROC, AUPRC) for all the holdouts.
holdout_total_number: int
The number of total holdouts.
Returns
------------------------
dict: dict[str, float]
The average metrics of the model accross all the holdouts for train and test."""
total_accuracy_train = 0
total_accuracy_test = 0
total_AUROC_train = 0
total_AUROC_test = 0
total_AUPRC_train = 0
total_AUPRC_test = 0
for holdout in model_metrics:
total_accuracy_train += holdout["train_evaluation"]['accuracy']
total_AUROC_train += holdout["train_evaluation"]['AUROC']
total_AUPRC_train += holdout["train_evaluation"]['AUPRC']
for holdout in model_metrics:
total_accuracy_test += holdout["test_evaluation"]['accuracy']
total_AUROC_test += holdout["test_evaluation"]['AUROC']
total_AUPRC_test += holdout["test_evaluation"]['AUPRC']
avg_accuracy_train = total_accuracy_train / holdout_total_number
avg_accuracy_test = total_accuracy_test / holdout_total_number
avg_AUROC_train = total_AUROC_train / holdout_total_number
avg_AUROC_test = total_AUROC_test / holdout_total_number
avg_AUPRC_train = total_AUPRC_train / holdout_total_number
avg_AUPRC_test = total_AUPRC_test / holdout_total_number
return {
'accuracy_train': avg_accuracy_train,
'accuracy_test': avg_accuracy_test,
'AUROC_train': avg_AUROC_train,
'AUROC_test': avg_AUROC_test,
'AUPRC_train': avg_AUPRC_train,
'AUPRC_test': avg_AUPRC_test,
}
def plot_train_history(model_history):
""" Plots the metrics (accuracy, AUROC, AUPRC) of the model across the epochs and for each holdout.
Parameters
------------------------
model_history: history
The training record of the model."""
for i in range(len(model_history)):
fig = plt.figure(figsize=(12.7,4.51))
fig.suptitle(str(i+1) + " Holdout")
# Accuracy
plt.subplot(1,3,1)
plt.plot(model_history[i].history['accuracy'])
plt.plot(model_history[i].history['val_accuracy'])
plt.title('Accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
# AUROC
plt.subplot(1,3,2)
plt.plot(model_history[i].history['AUROC'])
plt.plot(model_history[i].history['val_AUROC'])
plt.title('AUROC')
plt.ylabel('AUROC')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
# AUPRC
plt.subplot(1,3,3)
plt.plot(model_history[i].history['AUPRC'])
plt.plot(model_history[i].history['val_AUPRC'])
plt.title('AUPRC')
plt.ylabel('AUPRC')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.subplots_adjust(wspace=0.3)
plt.show()
Hyperparameter tuning is performed training a meta model over training and validation data to obtain the best set of hyperparameters via Bayesian optimization.
Here is the function to train the hypermodel.
def hyperparameter_tuning(
x_train: np.ndarray,
x_validation: np.ndarray,
y_train: np.ndarray,
y_validation: np.ndarray,
hypermodel: tf.keras.Model,
name: str,
directory: str,
max_trials: int = 10,
epochs: int = 50,
batch_size: int = 128
):
""" Tunes the hypermodel with bayesian optiomization and returns the best set of hyperparameters.
Parameters
------------------------
x_train: np.ndarray
The input data for training the hypermodel.
x_validation: np.ndarray
The input data for testing the hypermodel.
y_train: np.ndarray
The labels of the input data for training the hypermodel.
y_validation: np.ndarray
The labels of the input data for testing the hypermodel.
hypermodel: model
The hypermodel to tune or a function that builds a model using hp object.
name: str
The project name.
directory: str
The directory where to store results.
max_trials: int
The maximum amount of model configuration that the algorithm will try out.
epochs: int
The number of times the learning algorithm works through the dataset.
batch_size: int
The number of samples to work through before updating the model.
Returns
------------------------
hyperparameters: hyperparameters
The best set of hyperparameters."""
tuner = kt.tuners.BayesianOptimization(
hypermodel,
objective = [
kt.Objective("val_accuracy", direction="max"),
kt.Objective("val_AUROC", direction="max"),
kt.Objective("val_AUPRC", direction="max")],
max_trials = max_trials,
project_name = name,
directory = directory
)
tuner.search(
x_train,
y_train,
epochs = epochs,
batch_size = batch_size,
validation_data = (x_validation, y_validation),
callbacks = [EarlyStopping("val_loss", patience=2)]
)
best_hp = tuner.get_best_hyperparameters(num_trials=1)[0]
print(f"-- Best set of hyperparameters found:\n{best_hp.values}")
return best_hp
A multi-layer perceptron is a feed forward neural network consiting of one input and output layer with hidden dense layers in-between. To avoid overfitting of the model dropout layers were added.
This type of models is applied to classify audio genre given the 1D feature vectors.
Here are the functions to build the hypermodel for hyperparameter tuning and to build the model with the best set of hyperparameters found.
def build_MLP_hypermodel(hp) -> tf.keras.Model:
""" Returns the hypermodel.
Parameters
------------------------
hp: hyperparameters
The hyperparameters object.
Returns
------------------------
hypermodel: model
The hypermodel."""
MLP_Hypermodel = Sequential(name="MLP_Hypermodel")
MLP_Hypermodel.add(layers.Input((feature_number, )))
for i in range(hp.Int('depth', min_value=1, max_value=6)):
MLP_Hypermodel.add(layers.Dense(units = hp.Int('units_' + str(i), min_value=8, max_value=264, step=16), activation="relu"))
MLP_Hypermodel.add(layers.Dropout(hp.Choice('dropout_'+ str(i), [0.3, 0.4, 0.5])))
MLP_Hypermodel.add(layers.Dense(10, activation='softmax'))
MLP_Hypermodel.compile(
optimizer='nadam',
loss='categorical_crossentropy',
metrics=get_standard_binary_metrics()
)
MLP_Hypermodel.summary()
return MLP_Hypermodel
def build_MLP(
best_hyperparameters: dict,
input_shape: tuple
) -> tf.keras.Model:
""" Returns the model with the best set of hyperparameters.
Parameters
------------------------
best_hyperparameters: dict
The best set of hyperparameters for the model.
input_shape: tuple
The shape of the input.
Returns
------------------------
model: tf.keras.Model
The best model."""
MLP = Sequential(name="MLP")
MLP.add(layers.Input((input_shape[1], )))
for i in range(best_hyperparameters['depth']):
MLP.add(layers.Dense(units = best_hyperparameters['units_' + str(i)], activation="relu"))
MLP.add(layers.Dropout(best_hyperparameters['dropout_' + str(i)]))
MLP.add(layers.Dense(10, activation='softmax'))
MLP.compile(
optimizer='nadam',
loss='categorical_crossentropy',
metrics=get_standard_binary_metrics()
)
MLP.summary()
return MLP
To evaluate the performance of the optimized models and determine whether Bayesian optimization provides better performances, fixed hand-made architectures deemed to be good with respect to the complexity of the task are also defined for comparison.
def build_fixed_30s_MLP(
input_shape: tuple
) -> tf.keras.Model:
""" Returns the fixed MLP model for 30s window features.
Parameters
------------------------
input_shape: tuple
The shape of the input.
Returns
------------------------
model: model
The fixed model."""
MLP = Sequential(name="MLP")
MLP.add(layers.Input((input_shape[1], )))
MLP.add(layers.Dense(units = 128, activation="relu"))
MLP.add(layers.Dropout(0.4))
MLP.add(layers.Dense(units = 64, activation="relu"))
MLP.add(layers.Dropout(0.4))
MLP.add(layers.Dense(units = 64, activation="relu"))
MLP.add(layers.Dropout(0.4))
MLP.add(layers.Dense(10, activation='softmax'))
MLP.compile(
optimizer='nadam',
loss='categorical_crossentropy',
metrics=get_standard_binary_metrics()
)
MLP.summary()
return MLP
def build_fixed_3s_MLP(
input_shape: tuple
) -> tf.keras.Model:
""" Returns the fixed MLP model for 3s window features.
Parameters
------------------------
input_shape: tuple
The shape of the input.
Returns
------------------------
model: model
The fixed model."""
MLP = Sequential(name="MLP")
MLP.add(layers.Input((input_shape[1], )))
MLP.add(layers.Dense(units = 256, activation="relu"))
MLP.add(layers.Dropout(0.4))
MLP.add(layers.Dense(units = 256, activation="relu"))
MLP.add(layers.Dropout(0.4))
MLP.add(layers.Dense(units = 128, activation="relu"))
MLP.add(layers.Dropout(0.4))
MLP.add(layers.Dense(units = 128, activation="relu"))
MLP.add(layers.Dropout(0.4))
MLP.add(layers.Dense(10, activation='softmax'))
MLP.compile(
optimizer='nadam',
loss='categorical_crossentropy',
metrics=get_standard_binary_metrics()
)
MLP.summary()
return MLP
When using fixed hand-made architectures:
When applying hyperparameter tuning:
epochs = 100
batch_size = 128
feature_number = data['features_30s'].shape[1] #Same as data['features_3s'].shape
print("---- Fixed MLP ----")
input_data = {"window_30s": data['features_30s'], "window_3s": data['features_3s']}
data_labels = {"window_30s": labels_30s, "window_3s": labels_3s}
MLP_fixed_metrics = {}
MLP_fixed_history = {}
MLP_fixed_metrics["window_30s"] = []
MLP_fixed_metrics["window_3s"] = []
MLP_fixed_history["window_30s"] = []
MLP_fixed_history["window_3s"] = []
for window_type in ("window_30s", "window_3s"):
# Generate holdouts
for holdout_number, (train_indices, test_indices) in enumerate(tqdm(holdouts_generator.split(input_data[window_type], data_labels[window_type]))):
print(f"-- HOLDOUT {holdout_number+1} -- WINDOW {window_type}")
# Train/Test data
x_train, x_test = input_data[window_type].iloc[train_indices], input_data[window_type].iloc[test_indices]
y_train, y_test = data_labels[window_type].iloc[train_indices], data_labels[window_type].iloc[test_indices]
# Remove uncorrelated features with the output
uncorrelated_features = uncorrelated_features_test(x_train, y_train)
for feature in (x_train.columns):
if feature in (uncorrelated_features):
x_train = x_train.drop(columns=feature)
x_test = x_test.drop(columns=feature)
# Remove correlated features with eachother
correlated_features = correlated_features_test(x_train)
for feature in (x_train.columns):
if feature in (correlated_features):
x_train = x_train.drop(columns=feature)
x_test = x_test.drop(columns=feature)
# One-hot encoding
y_train = one_hot_encoding(y_train, 10)
y_test = one_hot_encoding(y_test, 10)
# Build fixed MLP
if window_type == "window_30s":
MLP = build_fixed_30s_MLP(x_train.shape)
else:
MLP = build_fixed_3s_MLP(x_train.shape)
print("- Training model:\n")
MLP_holdout_metrics, MLP_holdout_history = train_model(
MLP,
x_train.values,
x_test.values,
y_train.values,
y_test.values,
epochs,
batch_size
)
MLP_fixed_metrics[window_type].append(MLP_holdout_metrics)
MLP_fixed_history[window_type].append(MLP_holdout_history)
---- Fixed MLP ----
0it [00:00, ?it/s]
-- HOLDOUT 1 -- WINDOW window_30s
-- 21 Uncorrelated features: [Pearson+Spearman]
['mfcc16_mean', 'harmony_mean', 'mfcc10_var', 'mfcc4_mean', 'mfcc15_mean', 'mfcc19_mean', 'mfcc8_var', 'mfcc9_var', 'mfcc3_mean', 'chroma_stft_var', 'mfcc3_var', 'mfcc1_var', 'mfcc2_var', 'mfcc7_var', 'tempo', 'mfcc13_mean', 'mfcc5_mean', 'mfcc17_mean', 'mfcc14_mean', 'perceptr_mean', 'mfcc5_var']
-- Actually uncorrelated features: [confirmed via MIC]
[]
-- 1 Correlated features: [Pearson]
['spectral_centroid_mean']
Model: "MLP"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_180 (Dense) (None, 128) 7296
dropout_140 (Dropout) (None, 128) 0
dense_181 (Dense) (None, 64) 8256
dropout_141 (Dropout) (None, 64) 0
dense_182 (Dense) (None, 64) 4160
dropout_142 (Dropout) (None, 64) 0
dense_183 (Dense) (None, 10) 650
=================================================================
Total params: 20,362
Trainable params: 20,362
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
7/7 [==============================] - 2s 84ms/step - loss: 2.9128 - accuracy: 0.1314 - recall: 0.0125 - precision: 0.1176 - AUROC: 0.5104 - AUPRC: 0.1061 - f1_score: 0.0226 - balanced_accuracy: 0.5010 - specificity: 0.9896 - miss_rate: 0.9875 - fall_out: 0.0104 - mcc: 0.0061 - val_loss: 2.4489 - val_accuracy: 0.1400 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5483 - val_AUPRC: 0.1187 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.4994 - val_specificity: 0.9989 - val_miss_rate: 1.0000 - val_fall_out: 0.0011 - val_mcc: -0.0105
Epoch 2/100
7/7 [==============================] - 0s 16ms/step - loss: 2.5556 - accuracy: 0.1364 - recall: 0.0150 - precision: 0.2143 - AUROC: 0.5416 - AUPRC: 0.1173 - f1_score: 0.0281 - balanced_accuracy: 0.5045 - specificity: 0.9939 - miss_rate: 0.9850 - fall_out: 0.0061 - mcc: 0.0320 - val_loss: 2.3502 - val_accuracy: 0.2000 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5938 - val_AUPRC: 0.1434 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.4994 - val_specificity: 0.9989 - val_miss_rate: 1.0000 - val_fall_out: 0.0011 - val_mcc: -0.0105
Epoch 3/100
7/7 [==============================] - 0s 17ms/step - loss: 2.5108 - accuracy: 0.1527 - recall: 0.0088 - precision: 0.2000 - AUROC: 0.5595 - AUPRC: 0.1241 - f1_score: 0.0168 - balanced_accuracy: 0.5024 - specificity: 0.9961 - miss_rate: 0.9912 - fall_out: 0.0039 - mcc: 0.0221 - val_loss: 2.2978 - val_accuracy: 0.2600 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6247 - val_AUPRC: 0.1680 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.4997 - val_specificity: 0.9994 - val_miss_rate: 1.0000 - val_fall_out: 5.5556e-04 - val_mcc: -0.0075
Epoch 4/100
7/7 [==============================] - 0s 16ms/step - loss: 2.5151 - accuracy: 0.1464 - recall: 0.0075 - precision: 0.1714 - AUROC: 0.5681 - AUPRC: 0.1257 - f1_score: 0.0144 - balanced_accuracy: 0.5017 - specificity: 0.9960 - miss_rate: 0.9925 - fall_out: 0.0040 - mcc: 0.0158 - val_loss: 2.2558 - val_accuracy: 0.2700 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6709 - val_AUPRC: 0.1956 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.4997 - val_specificity: 0.9994 - val_miss_rate: 1.0000 - val_fall_out: 5.5556e-04 - val_mcc: -0.0075
Epoch 5/100
7/7 [==============================] - 0s 16ms/step - loss: 2.3441 - accuracy: 0.1527 - recall: 0.0088 - precision: 0.2500 - AUROC: 0.5922 - AUPRC: 0.1354 - f1_score: 0.0169 - balanced_accuracy: 0.5029 - specificity: 0.9971 - miss_rate: 0.9912 - fall_out: 0.0029 - mcc: 0.0297 - val_loss: 2.2227 - val_accuracy: 0.3050 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6986 - val_AUPRC: 0.2347 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 6/100
7/7 [==============================] - 0s 15ms/step - loss: 2.3924 - accuracy: 0.1589 - recall: 0.0100 - precision: 0.2963 - AUROC: 0.6000 - AUPRC: 0.1445 - f1_score: 0.0194 - balanced_accuracy: 0.5037 - specificity: 0.9974 - miss_rate: 0.9900 - fall_out: 0.0026 - mcc: 0.0381 - val_loss: 2.1914 - val_accuracy: 0.3100 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7140 - val_AUPRC: 0.2686 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 7/100
7/7 [==============================] - 0s 13ms/step - loss: 2.2304 - accuracy: 0.2253 - recall: 0.0088 - precision: 0.3182 - AUROC: 0.6514 - AUPRC: 0.1803 - f1_score: 0.0171 - balanced_accuracy: 0.5033 - specificity: 0.9979 - miss_rate: 0.9912 - fall_out: 0.0021 - mcc: 0.0382 - val_loss: 2.1535 - val_accuracy: 0.2900 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7387 - val_AUPRC: 0.3082 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 8/100
7/7 [==============================] - 0s 13ms/step - loss: 2.2371 - accuracy: 0.2190 - recall: 0.0138 - precision: 0.4231 - AUROC: 0.6586 - AUPRC: 0.1917 - f1_score: 0.0267 - balanced_accuracy: 0.5058 - specificity: 0.9979 - miss_rate: 0.9862 - fall_out: 0.0021 - mcc: 0.0615 - val_loss: 2.1084 - val_accuracy: 0.3250 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7635 - val_AUPRC: 0.3331 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 9/100
7/7 [==============================] - 0s 13ms/step - loss: 2.1417 - accuracy: 0.2578 - recall: 0.0175 - precision: 0.4118 - AUROC: 0.6864 - AUPRC: 0.2163 - f1_score: 0.0336 - balanced_accuracy: 0.5074 - specificity: 0.9972 - miss_rate: 0.9825 - fall_out: 0.0028 - mcc: 0.0679 - val_loss: 2.0608 - val_accuracy: 0.3350 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7785 - val_AUPRC: 0.3493 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 10/100
7/7 [==============================] - 0s 13ms/step - loss: 2.1853 - accuracy: 0.2365 - recall: 0.0300 - precision: 0.4898 - AUROC: 0.6848 - AUPRC: 0.2167 - f1_score: 0.0566 - balanced_accuracy: 0.5133 - specificity: 0.9965 - miss_rate: 0.9700 - fall_out: 0.0035 - mcc: 0.1021 - val_loss: 2.0186 - val_accuracy: 0.3450 - val_recall: 0.0100 - val_precision: 1.0000 - val_AUROC: 0.7896 - val_AUPRC: 0.3747 - val_f1_score: 0.0198 - val_balanced_accuracy: 0.5050 - val_specificity: 1.0000 - val_miss_rate: 0.9900 - val_fall_out: 0.0000e+00 - val_mcc: 0.0949
Epoch 11/100
7/7 [==============================] - 0s 15ms/step - loss: 2.1278 - accuracy: 0.2628 - recall: 0.0501 - precision: 0.6557 - AUROC: 0.6839 - AUPRC: 0.2405 - f1_score: 0.0930 - balanced_accuracy: 0.5236 - specificity: 0.9971 - miss_rate: 0.9499 - fall_out: 0.0029 - mcc: 0.1625 - val_loss: 1.9766 - val_accuracy: 0.3300 - val_recall: 0.0350 - val_precision: 1.0000 - val_AUROC: 0.7969 - val_AUPRC: 0.3895 - val_f1_score: 0.0676 - val_balanced_accuracy: 0.5175 - val_specificity: 1.0000 - val_miss_rate: 0.9650 - val_fall_out: 0.0000e+00 - val_mcc: 0.1778
Epoch 12/100
7/7 [==============================] - 0s 13ms/step - loss: 2.0997 - accuracy: 0.2778 - recall: 0.0613 - precision: 0.6806 - AUROC: 0.7023 - AUPRC: 0.2611 - f1_score: 0.1125 - balanced_accuracy: 0.5291 - specificity: 0.9968 - miss_rate: 0.9387 - fall_out: 0.0032 - mcc: 0.1845 - val_loss: 1.9412 - val_accuracy: 0.3350 - val_recall: 0.0500 - val_precision: 0.9091 - val_AUROC: 0.8110 - val_AUPRC: 0.3919 - val_f1_score: 0.0948 - val_balanced_accuracy: 0.5247 - val_specificity: 0.9994 - val_miss_rate: 0.9500 - val_fall_out: 5.5556e-04 - val_mcc: 0.2006
Epoch 13/100
7/7 [==============================] - 0s 13ms/step - loss: 2.0845 - accuracy: 0.2753 - recall: 0.0738 - precision: 0.6860 - AUROC: 0.7171 - AUPRC: 0.2699 - f1_score: 0.1333 - balanced_accuracy: 0.5350 - specificity: 0.9962 - miss_rate: 0.9262 - fall_out: 0.0038 - mcc: 0.2038 - val_loss: 1.9079 - val_accuracy: 0.3300 - val_recall: 0.0500 - val_precision: 0.9091 - val_AUROC: 0.8190 - val_AUPRC: 0.3975 - val_f1_score: 0.0948 - val_balanced_accuracy: 0.5247 - val_specificity: 0.9994 - val_miss_rate: 0.9500 - val_fall_out: 5.5556e-04 - val_mcc: 0.2006
Epoch 14/100
7/7 [==============================] - 0s 14ms/step - loss: 2.0071 - accuracy: 0.3016 - recall: 0.0776 - precision: 0.7381 - AUROC: 0.7417 - AUPRC: 0.3036 - f1_score: 0.1404 - balanced_accuracy: 0.5373 - specificity: 0.9969 - miss_rate: 0.9224 - fall_out: 0.0031 - mcc: 0.2192 - val_loss: 1.8712 - val_accuracy: 0.3550 - val_recall: 0.0700 - val_precision: 0.9333 - val_AUROC: 0.8284 - val_AUPRC: 0.4112 - val_f1_score: 0.1302 - val_balanced_accuracy: 0.5347 - val_specificity: 0.9994 - val_miss_rate: 0.9300 - val_fall_out: 5.5556e-04 - val_mcc: 0.2415
Epoch 15/100
7/7 [==============================] - 0s 12ms/step - loss: 1.9559 - accuracy: 0.3154 - recall: 0.0964 - precision: 0.6875 - AUROC: 0.7582 - AUPRC: 0.3182 - f1_score: 0.1690 - balanced_accuracy: 0.5458 - specificity: 0.9951 - miss_rate: 0.9036 - fall_out: 0.0049 - mcc: 0.2335 - val_loss: 1.8356 - val_accuracy: 0.3450 - val_recall: 0.0800 - val_precision: 0.8889 - val_AUROC: 0.8335 - val_AUPRC: 0.4171 - val_f1_score: 0.1468 - val_balanced_accuracy: 0.5394 - val_specificity: 0.9989 - val_miss_rate: 0.9200 - val_fall_out: 0.0011 - val_mcc: 0.2506
Epoch 16/100
7/7 [==============================] - 0s 13ms/step - loss: 1.9119 - accuracy: 0.3529 - recall: 0.1001 - precision: 0.6780 - AUROC: 0.7687 - AUPRC: 0.3404 - f1_score: 0.1745 - balanced_accuracy: 0.5474 - specificity: 0.9947 - miss_rate: 0.8999 - fall_out: 0.0053 - mcc: 0.2359 - val_loss: 1.8029 - val_accuracy: 0.3700 - val_recall: 0.1150 - val_precision: 0.9200 - val_AUROC: 0.8406 - val_AUPRC: 0.4279 - val_f1_score: 0.2044 - val_balanced_accuracy: 0.5569 - val_specificity: 0.9989 - val_miss_rate: 0.8850 - val_fall_out: 0.0011 - val_mcc: 0.3075
Epoch 17/100
7/7 [==============================] - 0s 12ms/step - loss: 1.8781 - accuracy: 0.3367 - recall: 0.1164 - precision: 0.6838 - AUROC: 0.7800 - AUPRC: 0.3574 - f1_score: 0.1989 - balanced_accuracy: 0.5552 - specificity: 0.9940 - miss_rate: 0.8836 - fall_out: 0.0060 - mcc: 0.2561 - val_loss: 1.7590 - val_accuracy: 0.3850 - val_recall: 0.1400 - val_precision: 0.8750 - val_AUROC: 0.8464 - val_AUPRC: 0.4380 - val_f1_score: 0.2414 - val_balanced_accuracy: 0.5689 - val_specificity: 0.9978 - val_miss_rate: 0.8600 - val_fall_out: 0.0022 - val_mcc: 0.3294
Epoch 18/100
7/7 [==============================] - 0s 13ms/step - loss: 1.8589 - accuracy: 0.3404 - recall: 0.1189 - precision: 0.6835 - AUROC: 0.7822 - AUPRC: 0.3525 - f1_score: 0.2026 - balanced_accuracy: 0.5564 - specificity: 0.9939 - miss_rate: 0.8811 - fall_out: 0.0061 - mcc: 0.2588 - val_loss: 1.7174 - val_accuracy: 0.3800 - val_recall: 0.1700 - val_precision: 0.8947 - val_AUROC: 0.8525 - val_AUPRC: 0.4533 - val_f1_score: 0.2857 - val_balanced_accuracy: 0.5839 - val_specificity: 0.9978 - val_miss_rate: 0.8300 - val_fall_out: 0.0022 - val_mcc: 0.3687
Epoch 19/100
7/7 [==============================] - 0s 12ms/step - loss: 1.8343 - accuracy: 0.3617 - recall: 0.1227 - precision: 0.6242 - AUROC: 0.8045 - AUPRC: 0.3607 - f1_score: 0.2050 - balanced_accuracy: 0.5572 - specificity: 0.9918 - miss_rate: 0.8773 - fall_out: 0.0082 - mcc: 0.2474 - val_loss: 1.6926 - val_accuracy: 0.4150 - val_recall: 0.1600 - val_precision: 0.8889 - val_AUROC: 0.8574 - val_AUPRC: 0.4609 - val_f1_score: 0.2712 - val_balanced_accuracy: 0.5789 - val_specificity: 0.9978 - val_miss_rate: 0.8400 - val_fall_out: 0.0022 - val_mcc: 0.3560
Epoch 20/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7800 - accuracy: 0.3655 - recall: 0.1402 - precision: 0.6667 - AUROC: 0.8117 - AUPRC: 0.3873 - f1_score: 0.2316 - balanced_accuracy: 0.5662 - specificity: 0.9922 - miss_rate: 0.8598 - fall_out: 0.0078 - mcc: 0.2768 - val_loss: 1.6683 - val_accuracy: 0.4100 - val_recall: 0.1700 - val_precision: 0.8718 - val_AUROC: 0.8615 - val_AUPRC: 0.4665 - val_f1_score: 0.2845 - val_balanced_accuracy: 0.5836 - val_specificity: 0.9972 - val_miss_rate: 0.8300 - val_fall_out: 0.0028 - val_mcc: 0.3628
Epoch 21/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7688 - accuracy: 0.3767 - recall: 0.1452 - precision: 0.6480 - AUROC: 0.8116 - AUPRC: 0.3901 - f1_score: 0.2372 - balanced_accuracy: 0.5682 - specificity: 0.9912 - miss_rate: 0.8548 - fall_out: 0.0088 - mcc: 0.2765 - val_loss: 1.6398 - val_accuracy: 0.4100 - val_recall: 0.1800 - val_precision: 0.8780 - val_AUROC: 0.8661 - val_AUPRC: 0.4770 - val_f1_score: 0.2988 - val_balanced_accuracy: 0.5886 - val_specificity: 0.9972 - val_miss_rate: 0.8200 - val_fall_out: 0.0028 - val_mcc: 0.3752
Epoch 22/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7451 - accuracy: 0.3642 - recall: 0.1314 - precision: 0.6213 - AUROC: 0.8180 - AUPRC: 0.3870 - f1_score: 0.2169 - balanced_accuracy: 0.5613 - specificity: 0.9911 - miss_rate: 0.8686 - fall_out: 0.0089 - mcc: 0.2554 - val_loss: 1.6089 - val_accuracy: 0.4250 - val_recall: 0.1800 - val_precision: 0.8780 - val_AUROC: 0.8700 - val_AUPRC: 0.4856 - val_f1_score: 0.2988 - val_balanced_accuracy: 0.5886 - val_specificity: 0.9972 - val_miss_rate: 0.8200 - val_fall_out: 0.0028 - val_mcc: 0.3752
Epoch 23/100
7/7 [==============================] - 0s 12ms/step - loss: 1.8163 - accuracy: 0.3817 - recall: 0.1502 - precision: 0.6349 - AUROC: 0.8039 - AUPRC: 0.3744 - f1_score: 0.2429 - balanced_accuracy: 0.5703 - specificity: 0.9904 - miss_rate: 0.8498 - fall_out: 0.0096 - mcc: 0.2775 - val_loss: 1.5835 - val_accuracy: 0.4250 - val_recall: 0.1800 - val_precision: 0.8182 - val_AUROC: 0.8738 - val_AUPRC: 0.4945 - val_f1_score: 0.2951 - val_balanced_accuracy: 0.5878 - val_specificity: 0.9956 - val_miss_rate: 0.8200 - val_fall_out: 0.0044 - val_mcc: 0.3591
Epoch 24/100
7/7 [==============================] - 0s 13ms/step - loss: 1.7241 - accuracy: 0.3767 - recall: 0.1577 - precision: 0.5860 - AUROC: 0.8239 - AUPRC: 0.3893 - f1_score: 0.2485 - balanced_accuracy: 0.5727 - specificity: 0.9876 - miss_rate: 0.8423 - fall_out: 0.0124 - mcc: 0.2694 - val_loss: 1.5691 - val_accuracy: 0.4450 - val_recall: 0.1900 - val_precision: 0.9268 - val_AUROC: 0.8773 - val_AUPRC: 0.5065 - val_f1_score: 0.3154 - val_balanced_accuracy: 0.5942 - val_specificity: 0.9983 - val_miss_rate: 0.8100 - val_fall_out: 0.0017 - val_mcc: 0.3987
Epoch 25/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7603 - accuracy: 0.3630 - recall: 0.1439 - precision: 0.6117 - AUROC: 0.8201 - AUPRC: 0.3844 - f1_score: 0.2330 - balanced_accuracy: 0.5669 - specificity: 0.9898 - miss_rate: 0.8561 - fall_out: 0.0102 - mcc: 0.2648 - val_loss: 1.5539 - val_accuracy: 0.4600 - val_recall: 0.1900 - val_precision: 0.9048 - val_AUROC: 0.8809 - val_AUPRC: 0.5149 - val_f1_score: 0.3140 - val_balanced_accuracy: 0.5939 - val_specificity: 0.9978 - val_miss_rate: 0.8100 - val_fall_out: 0.0022 - val_mcc: 0.3929
Epoch 26/100
7/7 [==============================] - 0s 15ms/step - loss: 1.7503 - accuracy: 0.3567 - recall: 0.1439 - precision: 0.6461 - AUROC: 0.8244 - AUPRC: 0.3950 - f1_score: 0.2354 - balanced_accuracy: 0.5676 - specificity: 0.9912 - miss_rate: 0.8561 - fall_out: 0.0088 - mcc: 0.2748 - val_loss: 1.5361 - val_accuracy: 0.4600 - val_recall: 0.1900 - val_precision: 0.8636 - val_AUROC: 0.8837 - val_AUPRC: 0.5202 - val_f1_score: 0.3115 - val_balanced_accuracy: 0.5933 - val_specificity: 0.9967 - val_miss_rate: 0.8100 - val_fall_out: 0.0033 - val_mcc: 0.3818
Epoch 27/100
7/7 [==============================] - 0s 14ms/step - loss: 1.6878 - accuracy: 0.4168 - recall: 0.1865 - precision: 0.6535 - AUROC: 0.8389 - AUPRC: 0.4218 - f1_score: 0.2902 - balanced_accuracy: 0.5877 - specificity: 0.9890 - miss_rate: 0.8135 - fall_out: 0.0110 - mcc: 0.3162 - val_loss: 1.5193 - val_accuracy: 0.4350 - val_recall: 0.1900 - val_precision: 0.8444 - val_AUROC: 0.8879 - val_AUPRC: 0.5225 - val_f1_score: 0.3102 - val_balanced_accuracy: 0.5931 - val_specificity: 0.9961 - val_miss_rate: 0.8100 - val_fall_out: 0.0039 - val_mcc: 0.3765
Epoch 28/100
7/7 [==============================] - 0s 14ms/step - loss: 1.6214 - accuracy: 0.4068 - recall: 0.1827 - precision: 0.6986 - AUROC: 0.8500 - AUPRC: 0.4528 - f1_score: 0.2897 - balanced_accuracy: 0.5870 - specificity: 0.9912 - miss_rate: 0.8173 - fall_out: 0.0088 - mcc: 0.3270 - val_loss: 1.4981 - val_accuracy: 0.4450 - val_recall: 0.2050 - val_precision: 0.8200 - val_AUROC: 0.8904 - val_AUPRC: 0.5286 - val_f1_score: 0.3280 - val_balanced_accuracy: 0.6000 - val_specificity: 0.9950 - val_miss_rate: 0.7950 - val_fall_out: 0.0050 - val_mcc: 0.3843
Epoch 29/100
7/7 [==============================] - 0s 14ms/step - loss: 1.5949 - accuracy: 0.4418 - recall: 0.1952 - precision: 0.6667 - AUROC: 0.8564 - AUPRC: 0.4567 - f1_score: 0.3020 - balanced_accuracy: 0.5922 - specificity: 0.9892 - miss_rate: 0.8048 - fall_out: 0.0108 - mcc: 0.3281 - val_loss: 1.4763 - val_accuracy: 0.4650 - val_recall: 0.2100 - val_precision: 0.8235 - val_AUROC: 0.8929 - val_AUPRC: 0.5389 - val_f1_score: 0.3347 - val_balanced_accuracy: 0.6025 - val_specificity: 0.9950 - val_miss_rate: 0.7900 - val_fall_out: 0.0050 - val_mcc: 0.3901
Epoch 30/100
7/7 [==============================] - 0s 13ms/step - loss: 1.6286 - accuracy: 0.4280 - recall: 0.1965 - precision: 0.6461 - AUROC: 0.8474 - AUPRC: 0.4398 - f1_score: 0.3013 - balanced_accuracy: 0.5923 - specificity: 0.9880 - miss_rate: 0.8035 - fall_out: 0.0120 - mcc: 0.3224 - val_loss: 1.4626 - val_accuracy: 0.4750 - val_recall: 0.2250 - val_precision: 0.8182 - val_AUROC: 0.8940 - val_AUPRC: 0.5437 - val_f1_score: 0.3529 - val_balanced_accuracy: 0.6097 - val_specificity: 0.9944 - val_miss_rate: 0.7750 - val_fall_out: 0.0056 - val_mcc: 0.4026
Epoch 31/100
7/7 [==============================] - 0s 13ms/step - loss: 1.5449 - accuracy: 0.4568 - recall: 0.2240 - precision: 0.6755 - AUROC: 0.8640 - AUPRC: 0.4729 - f1_score: 0.3365 - balanced_accuracy: 0.6060 - specificity: 0.9880 - miss_rate: 0.7760 - fall_out: 0.0120 - mcc: 0.3553 - val_loss: 1.4427 - val_accuracy: 0.4950 - val_recall: 0.2300 - val_precision: 0.7931 - val_AUROC: 0.8961 - val_AUPRC: 0.5521 - val_f1_score: 0.3566 - val_balanced_accuracy: 0.6117 - val_specificity: 0.9933 - val_miss_rate: 0.7700 - val_fall_out: 0.0067 - val_mcc: 0.3993
Epoch 32/100
7/7 [==============================] - 0s 12ms/step - loss: 1.5409 - accuracy: 0.4431 - recall: 0.2165 - precision: 0.6784 - AUROC: 0.8635 - AUPRC: 0.4780 - f1_score: 0.3283 - balanced_accuracy: 0.6026 - specificity: 0.9886 - miss_rate: 0.7835 - fall_out: 0.0114 - mcc: 0.3501 - val_loss: 1.4193 - val_accuracy: 0.5050 - val_recall: 0.2350 - val_precision: 0.8103 - val_AUROC: 0.8999 - val_AUPRC: 0.5639 - val_f1_score: 0.3643 - val_balanced_accuracy: 0.6144 - val_specificity: 0.9939 - val_miss_rate: 0.7650 - val_fall_out: 0.0061 - val_mcc: 0.4092
Epoch 33/100
7/7 [==============================] - 0s 13ms/step - loss: 1.5375 - accuracy: 0.4531 - recall: 0.2290 - precision: 0.7176 - AUROC: 0.8672 - AUPRC: 0.4859 - f1_score: 0.3472 - balanced_accuracy: 0.6095 - specificity: 0.9900 - miss_rate: 0.7710 - fall_out: 0.0100 - mcc: 0.3738 - val_loss: 1.4089 - val_accuracy: 0.5200 - val_recall: 0.2350 - val_precision: 0.7966 - val_AUROC: 0.9045 - val_AUPRC: 0.5741 - val_f1_score: 0.3629 - val_balanced_accuracy: 0.6142 - val_specificity: 0.9933 - val_miss_rate: 0.7650 - val_fall_out: 0.0067 - val_mcc: 0.4048
Epoch 34/100
7/7 [==============================] - 0s 13ms/step - loss: 1.5303 - accuracy: 0.4606 - recall: 0.2303 - precision: 0.6815 - AUROC: 0.8700 - AUPRC: 0.4739 - f1_score: 0.3442 - balanced_accuracy: 0.6092 - specificity: 0.9880 - miss_rate: 0.7697 - fall_out: 0.0120 - mcc: 0.3625 - val_loss: 1.4113 - val_accuracy: 0.5350 - val_recall: 0.2450 - val_precision: 0.8448 - val_AUROC: 0.9070 - val_AUPRC: 0.5803 - val_f1_score: 0.3798 - val_balanced_accuracy: 0.6200 - val_specificity: 0.9950 - val_miss_rate: 0.7550 - val_fall_out: 0.0050 - val_mcc: 0.4291
Epoch 35/100
7/7 [==============================] - 0s 14ms/step - loss: 1.5249 - accuracy: 0.4606 - recall: 0.2153 - precision: 0.6515 - AUROC: 0.8682 - AUPRC: 0.4786 - f1_score: 0.3236 - balanced_accuracy: 0.6012 - specificity: 0.9872 - miss_rate: 0.7847 - fall_out: 0.0128 - mcc: 0.3398 - val_loss: 1.4015 - val_accuracy: 0.5200 - val_recall: 0.2450 - val_precision: 0.8033 - val_AUROC: 0.9091 - val_AUPRC: 0.5842 - val_f1_score: 0.3755 - val_balanced_accuracy: 0.6192 - val_specificity: 0.9933 - val_miss_rate: 0.7550 - val_fall_out: 0.0067 - val_mcc: 0.4158
Epoch 36/100
7/7 [==============================] - 0s 14ms/step - loss: 1.4740 - accuracy: 0.4781 - recall: 0.2340 - precision: 0.6561 - AUROC: 0.8755 - AUPRC: 0.5028 - f1_score: 0.3450 - balanced_accuracy: 0.6102 - specificity: 0.9864 - miss_rate: 0.7660 - fall_out: 0.0136 - mcc: 0.3565 - val_loss: 1.3859 - val_accuracy: 0.5350 - val_recall: 0.2450 - val_precision: 0.7778 - val_AUROC: 0.9102 - val_AUPRC: 0.5893 - val_f1_score: 0.3726 - val_balanced_accuracy: 0.6186 - val_specificity: 0.9922 - val_miss_rate: 0.7550 - val_fall_out: 0.0078 - val_mcc: 0.4074
Epoch 37/100
7/7 [==============================] - 0s 12ms/step - loss: 1.5101 - accuracy: 0.4931 - recall: 0.2365 - precision: 0.7159 - AUROC: 0.8751 - AUPRC: 0.5146 - f1_score: 0.3556 - balanced_accuracy: 0.6131 - specificity: 0.9896 - miss_rate: 0.7635 - fall_out: 0.0104 - mcc: 0.3795 - val_loss: 1.3733 - val_accuracy: 0.5500 - val_recall: 0.2750 - val_precision: 0.7857 - val_AUROC: 0.9107 - val_AUPRC: 0.5930 - val_f1_score: 0.4074 - val_balanced_accuracy: 0.6333 - val_specificity: 0.9917 - val_miss_rate: 0.7250 - val_fall_out: 0.0083 - val_mcc: 0.4353
Epoch 38/100
7/7 [==============================] - 0s 12ms/step - loss: 1.5029 - accuracy: 0.4768 - recall: 0.2503 - precision: 0.6897 - AUROC: 0.8727 - AUPRC: 0.5078 - f1_score: 0.3673 - balanced_accuracy: 0.6189 - specificity: 0.9875 - miss_rate: 0.7497 - fall_out: 0.0125 - mcc: 0.3814 - val_loss: 1.3656 - val_accuracy: 0.5550 - val_recall: 0.2650 - val_precision: 0.7681 - val_AUROC: 0.9116 - val_AUPRC: 0.5937 - val_f1_score: 0.3941 - val_balanced_accuracy: 0.6281 - val_specificity: 0.9911 - val_miss_rate: 0.7350 - val_fall_out: 0.0089 - val_mcc: 0.4210
Epoch 39/100
7/7 [==============================] - 0s 13ms/step - loss: 1.4335 - accuracy: 0.4831 - recall: 0.2603 - precision: 0.6980 - AUROC: 0.8837 - AUPRC: 0.5322 - f1_score: 0.3792 - balanced_accuracy: 0.6239 - specificity: 0.9875 - miss_rate: 0.7397 - fall_out: 0.0125 - mcc: 0.3923 - val_loss: 1.3386 - val_accuracy: 0.5550 - val_recall: 0.2750 - val_precision: 0.7746 - val_AUROC: 0.9156 - val_AUPRC: 0.6040 - val_f1_score: 0.4059 - val_balanced_accuracy: 0.6331 - val_specificity: 0.9911 - val_miss_rate: 0.7250 - val_fall_out: 0.0089 - val_mcc: 0.4314
Epoch 40/100
7/7 [==============================] - 0s 13ms/step - loss: 1.4446 - accuracy: 0.4956 - recall: 0.2666 - precision: 0.7500 - AUROC: 0.8828 - AUPRC: 0.5322 - f1_score: 0.3934 - balanced_accuracy: 0.6284 - specificity: 0.9901 - miss_rate: 0.7334 - fall_out: 0.0099 - mcc: 0.4159 - val_loss: 1.3240 - val_accuracy: 0.5650 - val_recall: 0.2950 - val_precision: 0.7763 - val_AUROC: 0.9163 - val_AUPRC: 0.6112 - val_f1_score: 0.4275 - val_balanced_accuracy: 0.6428 - val_specificity: 0.9906 - val_miss_rate: 0.7050 - val_fall_out: 0.0094 - val_mcc: 0.4481
Epoch 41/100
7/7 [==============================] - 0s 13ms/step - loss: 1.4160 - accuracy: 0.4994 - recall: 0.2766 - precision: 0.7543 - AUROC: 0.8862 - AUPRC: 0.5463 - f1_score: 0.4048 - balanced_accuracy: 0.6333 - specificity: 0.9900 - miss_rate: 0.7234 - fall_out: 0.0100 - mcc: 0.4255 - val_loss: 1.3112 - val_accuracy: 0.5850 - val_recall: 0.2950 - val_precision: 0.8082 - val_AUROC: 0.9173 - val_AUPRC: 0.6182 - val_f1_score: 0.4322 - val_balanced_accuracy: 0.6436 - val_specificity: 0.9922 - val_miss_rate: 0.7050 - val_fall_out: 0.0078 - val_mcc: 0.4595
Epoch 42/100
7/7 [==============================] - 0s 14ms/step - loss: 1.4019 - accuracy: 0.5181 - recall: 0.2916 - precision: 0.7819 - AUROC: 0.8885 - AUPRC: 0.5627 - f1_score: 0.4248 - balanced_accuracy: 0.6413 - specificity: 0.9910 - miss_rate: 0.7084 - fall_out: 0.0090 - mcc: 0.4474 - val_loss: 1.2913 - val_accuracy: 0.5800 - val_recall: 0.3200 - val_precision: 0.7901 - val_AUROC: 0.9198 - val_AUPRC: 0.6258 - val_f1_score: 0.4555 - val_balanced_accuracy: 0.6553 - val_specificity: 0.9906 - val_miss_rate: 0.6800 - val_fall_out: 0.0094 - val_mcc: 0.4726
Epoch 43/100
7/7 [==============================] - 0s 14ms/step - loss: 1.3650 - accuracy: 0.5294 - recall: 0.2829 - precision: 0.7338 - AUROC: 0.8969 - AUPRC: 0.5702 - f1_score: 0.4083 - balanced_accuracy: 0.6357 - specificity: 0.9886 - miss_rate: 0.7171 - fall_out: 0.0114 - mcc: 0.4230 - val_loss: 1.2714 - val_accuracy: 0.5800 - val_recall: 0.3300 - val_precision: 0.7857 - val_AUROC: 0.9219 - val_AUPRC: 0.6318 - val_f1_score: 0.4648 - val_balanced_accuracy: 0.6600 - val_specificity: 0.9900 - val_miss_rate: 0.6700 - val_fall_out: 0.0100 - val_mcc: 0.4786
Epoch 44/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3104 - accuracy: 0.5307 - recall: 0.3091 - precision: 0.7037 - AUROC: 0.9031 - AUPRC: 0.5820 - f1_score: 0.4296 - balanced_accuracy: 0.6473 - specificity: 0.9855 - miss_rate: 0.6909 - fall_out: 0.0145 - mcc: 0.4314 - val_loss: 1.2609 - val_accuracy: 0.5900 - val_recall: 0.3250 - val_precision: 0.7738 - val_AUROC: 0.9236 - val_AUPRC: 0.6358 - val_f1_score: 0.4577 - val_balanced_accuracy: 0.6572 - val_specificity: 0.9894 - val_miss_rate: 0.6750 - val_fall_out: 0.0106 - val_mcc: 0.4703
Epoch 45/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3157 - accuracy: 0.5369 - recall: 0.3141 - precision: 0.7470 - AUROC: 0.9025 - AUPRC: 0.5920 - f1_score: 0.4423 - balanced_accuracy: 0.6512 - specificity: 0.9882 - miss_rate: 0.6859 - fall_out: 0.0118 - mcc: 0.4519 - val_loss: 1.2520 - val_accuracy: 0.6050 - val_recall: 0.3300 - val_precision: 0.7765 - val_AUROC: 0.9240 - val_AUPRC: 0.6365 - val_f1_score: 0.4632 - val_balanced_accuracy: 0.6597 - val_specificity: 0.9894 - val_miss_rate: 0.6700 - val_fall_out: 0.0106 - val_mcc: 0.4751
Epoch 46/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3333 - accuracy: 0.5131 - recall: 0.3242 - precision: 0.7296 - AUROC: 0.8993 - AUPRC: 0.5782 - f1_score: 0.4489 - balanced_accuracy: 0.6554 - specificity: 0.9866 - miss_rate: 0.6758 - fall_out: 0.0134 - mcc: 0.4525 - val_loss: 1.2377 - val_accuracy: 0.6000 - val_recall: 0.3350 - val_precision: 0.7614 - val_AUROC: 0.9253 - val_AUPRC: 0.6379 - val_f1_score: 0.4653 - val_balanced_accuracy: 0.6617 - val_specificity: 0.9883 - val_miss_rate: 0.6650 - val_fall_out: 0.0117 - val_mcc: 0.4730
Epoch 47/100
7/7 [==============================] - 0s 13ms/step - loss: 1.3207 - accuracy: 0.5382 - recall: 0.3166 - precision: 0.7355 - AUROC: 0.9018 - AUPRC: 0.5735 - f1_score: 0.4427 - balanced_accuracy: 0.6520 - specificity: 0.9873 - miss_rate: 0.6834 - fall_out: 0.0127 - mcc: 0.4493 - val_loss: 1.2328 - val_accuracy: 0.6000 - val_recall: 0.3300 - val_precision: 0.7500 - val_AUROC: 0.9254 - val_AUPRC: 0.6381 - val_f1_score: 0.4583 - val_balanced_accuracy: 0.6589 - val_specificity: 0.9878 - val_miss_rate: 0.6700 - val_fall_out: 0.0122 - val_mcc: 0.4648
Epoch 48/100
7/7 [==============================] - 0s 13ms/step - loss: 1.2929 - accuracy: 0.5432 - recall: 0.3267 - precision: 0.7373 - AUROC: 0.9067 - AUPRC: 0.5978 - f1_score: 0.4527 - balanced_accuracy: 0.6569 - specificity: 0.9871 - miss_rate: 0.6733 - fall_out: 0.0129 - mcc: 0.4574 - val_loss: 1.2251 - val_accuracy: 0.6050 - val_recall: 0.3450 - val_precision: 0.7582 - val_AUROC: 0.9259 - val_AUPRC: 0.6421 - val_f1_score: 0.4742 - val_balanced_accuracy: 0.6664 - val_specificity: 0.9878 - val_miss_rate: 0.6550 - val_fall_out: 0.0122 - val_mcc: 0.4791
Epoch 49/100
7/7 [==============================] - 0s 14ms/step - loss: 1.2996 - accuracy: 0.5494 - recall: 0.3655 - precision: 0.7565 - AUROC: 0.9031 - AUPRC: 0.6169 - f1_score: 0.4928 - balanced_accuracy: 0.6762 - specificity: 0.9869 - miss_rate: 0.6345 - fall_out: 0.0131 - mcc: 0.4930 - val_loss: 1.2152 - val_accuracy: 0.6250 - val_recall: 0.3750 - val_precision: 0.7812 - val_AUROC: 0.9282 - val_AUPRC: 0.6517 - val_f1_score: 0.5068 - val_balanced_accuracy: 0.6817 - val_specificity: 0.9883 - val_miss_rate: 0.6250 - val_fall_out: 0.0117 - val_mcc: 0.5099
Epoch 50/100
7/7 [==============================] - 0s 14ms/step - loss: 1.2686 - accuracy: 0.5344 - recall: 0.3554 - precision: 0.7339 - AUROC: 0.9113 - AUPRC: 0.6063 - f1_score: 0.4789 - balanced_accuracy: 0.6706 - specificity: 0.9857 - miss_rate: 0.6446 - fall_out: 0.0143 - mcc: 0.4767 - val_loss: 1.2064 - val_accuracy: 0.6250 - val_recall: 0.3750 - val_precision: 0.7653 - val_AUROC: 0.9299 - val_AUPRC: 0.6580 - val_f1_score: 0.5034 - val_balanced_accuracy: 0.6811 - val_specificity: 0.9872 - val_miss_rate: 0.6250 - val_fall_out: 0.0128 - val_mcc: 0.5034
Epoch 51/100
7/7 [==============================] - 0s 13ms/step - loss: 1.2706 - accuracy: 0.5519 - recall: 0.3329 - precision: 0.7578 - AUROC: 0.9080 - AUPRC: 0.6146 - f1_score: 0.4626 - balanced_accuracy: 0.6605 - specificity: 0.9882 - miss_rate: 0.6671 - fall_out: 0.0118 - mcc: 0.4700 - val_loss: 1.2045 - val_accuracy: 0.6150 - val_recall: 0.3500 - val_precision: 0.7292 - val_AUROC: 0.9294 - val_AUPRC: 0.6514 - val_f1_score: 0.4730 - val_balanced_accuracy: 0.6678 - val_specificity: 0.9856 - val_miss_rate: 0.6500 - val_fall_out: 0.0144 - val_mcc: 0.4709
Epoch 52/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2323 - accuracy: 0.5657 - recall: 0.3592 - precision: 0.7633 - AUROC: 0.9156 - AUPRC: 0.6321 - f1_score: 0.4885 - balanced_accuracy: 0.6734 - specificity: 0.9876 - miss_rate: 0.6408 - fall_out: 0.0124 - mcc: 0.4913 - val_loss: 1.1959 - val_accuracy: 0.6250 - val_recall: 0.3650 - val_precision: 0.7157 - val_AUROC: 0.9300 - val_AUPRC: 0.6607 - val_f1_score: 0.4834 - val_balanced_accuracy: 0.6744 - val_specificity: 0.9839 - val_miss_rate: 0.6350 - val_fall_out: 0.0161 - val_mcc: 0.4758
Epoch 53/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2477 - accuracy: 0.5620 - recall: 0.3542 - precision: 0.7567 - AUROC: 0.9136 - AUPRC: 0.6236 - f1_score: 0.4825 - balanced_accuracy: 0.6708 - specificity: 0.9873 - miss_rate: 0.6458 - fall_out: 0.0127 - mcc: 0.4851 - val_loss: 1.1735 - val_accuracy: 0.6300 - val_recall: 0.3850 - val_precision: 0.7778 - val_AUROC: 0.9312 - val_AUPRC: 0.6678 - val_f1_score: 0.5151 - val_balanced_accuracy: 0.6864 - val_specificity: 0.9878 - val_miss_rate: 0.6150 - val_fall_out: 0.0122 - val_mcc: 0.5156
Epoch 54/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3030 - accuracy: 0.5407 - recall: 0.3429 - precision: 0.7230 - AUROC: 0.9065 - AUPRC: 0.5999 - f1_score: 0.4652 - balanced_accuracy: 0.6642 - specificity: 0.9854 - miss_rate: 0.6571 - fall_out: 0.0146 - mcc: 0.4634 - val_loss: 1.1662 - val_accuracy: 0.6250 - val_recall: 0.3850 - val_precision: 0.7778 - val_AUROC: 0.9318 - val_AUPRC: 0.6679 - val_f1_score: 0.5151 - val_balanced_accuracy: 0.6864 - val_specificity: 0.9878 - val_miss_rate: 0.6150 - val_fall_out: 0.0122 - val_mcc: 0.5156
Epoch 55/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2125 - accuracy: 0.5594 - recall: 0.3805 - precision: 0.7238 - AUROC: 0.9207 - AUPRC: 0.6289 - f1_score: 0.4988 - balanced_accuracy: 0.6822 - specificity: 0.9839 - miss_rate: 0.6195 - fall_out: 0.0161 - mcc: 0.4898 - val_loss: 1.1642 - val_accuracy: 0.6350 - val_recall: 0.3900 - val_precision: 0.7879 - val_AUROC: 0.9313 - val_AUPRC: 0.6716 - val_f1_score: 0.5217 - val_balanced_accuracy: 0.6892 - val_specificity: 0.9883 - val_miss_rate: 0.6100 - val_fall_out: 0.0117 - val_mcc: 0.5233
Epoch 56/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2014 - accuracy: 0.5670 - recall: 0.3767 - precision: 0.7306 - AUROC: 0.9195 - AUPRC: 0.6395 - f1_score: 0.4971 - balanced_accuracy: 0.6806 - specificity: 0.9846 - miss_rate: 0.6233 - fall_out: 0.0154 - mcc: 0.4901 - val_loss: 1.1567 - val_accuracy: 0.6300 - val_recall: 0.3800 - val_precision: 0.7677 - val_AUROC: 0.9321 - val_AUPRC: 0.6729 - val_f1_score: 0.5084 - val_balanced_accuracy: 0.6836 - val_specificity: 0.9872 - val_miss_rate: 0.6200 - val_fall_out: 0.0128 - val_mcc: 0.5079
Epoch 57/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2095 - accuracy: 0.5982 - recall: 0.3830 - precision: 0.7593 - AUROC: 0.9174 - AUPRC: 0.6402 - f1_score: 0.5092 - balanced_accuracy: 0.6847 - specificity: 0.9865 - miss_rate: 0.6170 - fall_out: 0.0135 - mcc: 0.5065 - val_loss: 1.1444 - val_accuracy: 0.6300 - val_recall: 0.3900 - val_precision: 0.7800 - val_AUROC: 0.9330 - val_AUPRC: 0.6773 - val_f1_score: 0.5200 - val_balanced_accuracy: 0.6889 - val_specificity: 0.9878 - val_miss_rate: 0.6100 - val_fall_out: 0.0122 - val_mcc: 0.5200
Epoch 58/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2421 - accuracy: 0.5782 - recall: 0.3717 - precision: 0.7577 - AUROC: 0.9113 - AUPRC: 0.6338 - f1_score: 0.4987 - balanced_accuracy: 0.6793 - specificity: 0.9868 - miss_rate: 0.6283 - fall_out: 0.0132 - mcc: 0.4979 - val_loss: 1.1398 - val_accuracy: 0.6350 - val_recall: 0.3850 - val_precision: 0.7700 - val_AUROC: 0.9340 - val_AUPRC: 0.6807 - val_f1_score: 0.5133 - val_balanced_accuracy: 0.6861 - val_specificity: 0.9872 - val_miss_rate: 0.6150 - val_fall_out: 0.0128 - val_mcc: 0.5124
Epoch 59/100
7/7 [==============================] - 0s 14ms/step - loss: 1.1957 - accuracy: 0.5845 - recall: 0.3705 - precision: 0.7327 - AUROC: 0.9226 - AUPRC: 0.6372 - f1_score: 0.4921 - balanced_accuracy: 0.6777 - specificity: 0.9850 - miss_rate: 0.6295 - fall_out: 0.0150 - mcc: 0.4867 - val_loss: 1.1328 - val_accuracy: 0.6250 - val_recall: 0.4000 - val_precision: 0.7692 - val_AUROC: 0.9338 - val_AUPRC: 0.6822 - val_f1_score: 0.5263 - val_balanced_accuracy: 0.6933 - val_specificity: 0.9867 - val_miss_rate: 0.6000 - val_fall_out: 0.0133 - val_mcc: 0.5225
Epoch 60/100
7/7 [==============================] - 0s 13ms/step - loss: 1.1816 - accuracy: 0.5832 - recall: 0.3980 - precision: 0.7589 - AUROC: 0.9220 - AUPRC: 0.6494 - f1_score: 0.5222 - balanced_accuracy: 0.6920 - specificity: 0.9860 - miss_rate: 0.6020 - fall_out: 0.0140 - mcc: 0.5167 - val_loss: 1.1271 - val_accuracy: 0.6300 - val_recall: 0.4350 - val_precision: 0.7909 - val_AUROC: 0.9347 - val_AUPRC: 0.6850 - val_f1_score: 0.5613 - val_balanced_accuracy: 0.7111 - val_specificity: 0.9872 - val_miss_rate: 0.5650 - val_fall_out: 0.0128 - val_mcc: 0.5556
Epoch 61/100
7/7 [==============================] - 0s 12ms/step - loss: 1.0997 - accuracy: 0.6120 - recall: 0.4093 - precision: 0.7956 - AUROC: 0.9334 - AUPRC: 0.6918 - f1_score: 0.5405 - balanced_accuracy: 0.6988 - specificity: 0.9883 - miss_rate: 0.5907 - fall_out: 0.0117 - mcc: 0.5400 - val_loss: 1.1228 - val_accuracy: 0.6300 - val_recall: 0.4550 - val_precision: 0.7712 - val_AUROC: 0.9350 - val_AUPRC: 0.6883 - val_f1_score: 0.5723 - val_balanced_accuracy: 0.7200 - val_specificity: 0.9850 - val_miss_rate: 0.5450 - val_fall_out: 0.0150 - val_mcc: 0.5602
Epoch 62/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1776 - accuracy: 0.5920 - recall: 0.4130 - precision: 0.7876 - AUROC: 0.9227 - AUPRC: 0.6547 - f1_score: 0.5419 - balanced_accuracy: 0.7003 - specificity: 0.9876 - miss_rate: 0.5870 - fall_out: 0.0124 - mcc: 0.5392 - val_loss: 1.1229 - val_accuracy: 0.6350 - val_recall: 0.4650 - val_precision: 0.7750 - val_AUROC: 0.9347 - val_AUPRC: 0.6905 - val_f1_score: 0.5813 - val_balanced_accuracy: 0.7250 - val_specificity: 0.9850 - val_miss_rate: 0.5350 - val_fall_out: 0.0150 - val_mcc: 0.5685
Epoch 63/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1804 - accuracy: 0.6008 - recall: 0.3955 - precision: 0.7524 - AUROC: 0.9218 - AUPRC: 0.6557 - f1_score: 0.5185 - balanced_accuracy: 0.6905 - specificity: 0.9855 - miss_rate: 0.6045 - fall_out: 0.0145 - mcc: 0.5122 - val_loss: 1.1196 - val_accuracy: 0.6450 - val_recall: 0.4800 - val_precision: 0.7619 - val_AUROC: 0.9351 - val_AUPRC: 0.6883 - val_f1_score: 0.5890 - val_balanced_accuracy: 0.7317 - val_specificity: 0.9833 - val_miss_rate: 0.5200 - val_fall_out: 0.0167 - val_mcc: 0.5721
Epoch 64/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1672 - accuracy: 0.5770 - recall: 0.4093 - precision: 0.7552 - AUROC: 0.9245 - AUPRC: 0.6551 - f1_score: 0.5308 - balanced_accuracy: 0.6973 - specificity: 0.9853 - miss_rate: 0.5907 - fall_out: 0.0147 - mcc: 0.5228 - val_loss: 1.1186 - val_accuracy: 0.6400 - val_recall: 0.4700 - val_precision: 0.7642 - val_AUROC: 0.9361 - val_AUPRC: 0.6919 - val_f1_score: 0.5820 - val_balanced_accuracy: 0.7269 - val_specificity: 0.9839 - val_miss_rate: 0.5300 - val_fall_out: 0.0161 - val_mcc: 0.5668
Epoch 65/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1353 - accuracy: 0.5995 - recall: 0.4193 - precision: 0.7737 - AUROC: 0.9283 - AUPRC: 0.6690 - f1_score: 0.5438 - balanced_accuracy: 0.7028 - specificity: 0.9864 - miss_rate: 0.5807 - fall_out: 0.0136 - mcc: 0.5375 - val_loss: 1.1145 - val_accuracy: 0.6500 - val_recall: 0.4750 - val_precision: 0.7724 - val_AUROC: 0.9376 - val_AUPRC: 0.6961 - val_f1_score: 0.5882 - val_balanced_accuracy: 0.7297 - val_specificity: 0.9844 - val_miss_rate: 0.5250 - val_fall_out: 0.0156 - val_mcc: 0.5737
Epoch 66/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1063 - accuracy: 0.6033 - recall: 0.4318 - precision: 0.7718 - AUROC: 0.9327 - AUPRC: 0.6827 - f1_score: 0.5538 - balanced_accuracy: 0.7088 - specificity: 0.9858 - miss_rate: 0.5682 - fall_out: 0.0142 - mcc: 0.5451 - val_loss: 1.1022 - val_accuracy: 0.6450 - val_recall: 0.4700 - val_precision: 0.7833 - val_AUROC: 0.9390 - val_AUPRC: 0.7003 - val_f1_score: 0.5875 - val_balanced_accuracy: 0.7278 - val_specificity: 0.9856 - val_miss_rate: 0.5300 - val_fall_out: 0.0144 - val_mcc: 0.5755
Epoch 67/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1086 - accuracy: 0.5907 - recall: 0.4255 - precision: 0.7623 - AUROC: 0.9331 - AUPRC: 0.6726 - f1_score: 0.5462 - balanced_accuracy: 0.7054 - specificity: 0.9853 - miss_rate: 0.5745 - fall_out: 0.0147 - mcc: 0.5368 - val_loss: 1.0930 - val_accuracy: 0.6500 - val_recall: 0.4700 - val_precision: 0.7769 - val_AUROC: 0.9404 - val_AUPRC: 0.7003 - val_f1_score: 0.5857 - val_balanced_accuracy: 0.7275 - val_specificity: 0.9850 - val_miss_rate: 0.5300 - val_fall_out: 0.0150 - val_mcc: 0.5725
Epoch 68/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1190 - accuracy: 0.6195 - recall: 0.4380 - precision: 0.7726 - AUROC: 0.9305 - AUPRC: 0.6829 - f1_score: 0.5591 - balanced_accuracy: 0.7119 - specificity: 0.9857 - miss_rate: 0.5620 - fall_out: 0.0143 - mcc: 0.5497 - val_loss: 1.1007 - val_accuracy: 0.6550 - val_recall: 0.4600 - val_precision: 0.7541 - val_AUROC: 0.9391 - val_AUPRC: 0.6999 - val_f1_score: 0.5714 - val_balanced_accuracy: 0.7217 - val_specificity: 0.9833 - val_miss_rate: 0.5400 - val_fall_out: 0.0167 - val_mcc: 0.5557
Epoch 69/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1006 - accuracy: 0.5920 - recall: 0.4118 - precision: 0.7759 - AUROC: 0.9333 - AUPRC: 0.6882 - f1_score: 0.5380 - balanced_accuracy: 0.6993 - specificity: 0.9868 - miss_rate: 0.5882 - fall_out: 0.0132 - mcc: 0.5334 - val_loss: 1.0822 - val_accuracy: 0.6550 - val_recall: 0.4700 - val_precision: 0.7705 - val_AUROC: 0.9402 - val_AUPRC: 0.7043 - val_f1_score: 0.5839 - val_balanced_accuracy: 0.7272 - val_specificity: 0.9844 - val_miss_rate: 0.5300 - val_fall_out: 0.0156 - val_mcc: 0.5696
Epoch 70/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1324 - accuracy: 0.5982 - recall: 0.4318 - precision: 0.7805 - AUROC: 0.9281 - AUPRC: 0.6730 - f1_score: 0.5560 - balanced_accuracy: 0.7092 - specificity: 0.9865 - miss_rate: 0.5682 - fall_out: 0.0135 - mcc: 0.5489 - val_loss: 1.0750 - val_accuracy: 0.6550 - val_recall: 0.4700 - val_precision: 0.7642 - val_AUROC: 0.9412 - val_AUPRC: 0.7092 - val_f1_score: 0.5820 - val_balanced_accuracy: 0.7269 - val_specificity: 0.9839 - val_miss_rate: 0.5300 - val_fall_out: 0.0161 - val_mcc: 0.5668
Epoch 71/100
7/7 [==============================] - 0s 14ms/step - loss: 1.0878 - accuracy: 0.6270 - recall: 0.4418 - precision: 0.7968 - AUROC: 0.9338 - AUPRC: 0.6900 - f1_score: 0.5684 - balanced_accuracy: 0.7146 - specificity: 0.9875 - miss_rate: 0.5582 - fall_out: 0.0125 - mcc: 0.5628 - val_loss: 1.0709 - val_accuracy: 0.6600 - val_recall: 0.4700 - val_precision: 0.7520 - val_AUROC: 0.9412 - val_AUPRC: 0.7119 - val_f1_score: 0.5785 - val_balanced_accuracy: 0.7264 - val_specificity: 0.9828 - val_miss_rate: 0.5300 - val_fall_out: 0.0172 - val_mcc: 0.5612
Epoch 72/100
7/7 [==============================] - 0s 13ms/step - loss: 1.0754 - accuracy: 0.6195 - recall: 0.4305 - precision: 0.7627 - AUROC: 0.9372 - AUPRC: 0.6922 - f1_score: 0.5504 - balanced_accuracy: 0.7078 - specificity: 0.9851 - miss_rate: 0.5695 - fall_out: 0.0149 - mcc: 0.5403 - val_loss: 1.0605 - val_accuracy: 0.6500 - val_recall: 0.4700 - val_precision: 0.7769 - val_AUROC: 0.9419 - val_AUPRC: 0.7163 - val_f1_score: 0.5857 - val_balanced_accuracy: 0.7275 - val_specificity: 0.9850 - val_miss_rate: 0.5300 - val_fall_out: 0.0150 - val_mcc: 0.5725
Epoch 73/100
7/7 [==============================] - 0s 14ms/step - loss: 1.0226 - accuracy: 0.6395 - recall: 0.4643 - precision: 0.7944 - AUROC: 0.9420 - AUPRC: 0.7209 - f1_score: 0.5861 - balanced_accuracy: 0.7255 - specificity: 0.9866 - miss_rate: 0.5357 - fall_out: 0.0134 - mcc: 0.5767 - val_loss: 1.0566 - val_accuracy: 0.6500 - val_recall: 0.4800 - val_precision: 0.7805 - val_AUROC: 0.9424 - val_AUPRC: 0.7179 - val_f1_score: 0.5944 - val_balanced_accuracy: 0.7325 - val_specificity: 0.9850 - val_miss_rate: 0.5200 - val_fall_out: 0.0150 - val_mcc: 0.5807
Epoch 74/100
7/7 [==============================] - 0s 13ms/step - loss: 1.0514 - accuracy: 0.6295 - recall: 0.4506 - precision: 0.7759 - AUROC: 0.9387 - AUPRC: 0.7034 - f1_score: 0.5701 - balanced_accuracy: 0.7181 - specificity: 0.9855 - miss_rate: 0.5494 - fall_out: 0.0145 - mcc: 0.5594 - val_loss: 1.0673 - val_accuracy: 0.6550 - val_recall: 0.5050 - val_precision: 0.7953 - val_AUROC: 0.9409 - val_AUPRC: 0.7179 - val_f1_score: 0.6177 - val_balanced_accuracy: 0.7453 - val_specificity: 0.9856 - val_miss_rate: 0.4950 - val_fall_out: 0.0144 - val_mcc: 0.6035
Epoch 75/100
7/7 [==============================] - 0s 12ms/step - loss: 1.0212 - accuracy: 0.6521 - recall: 0.4956 - precision: 0.8082 - AUROC: 0.9422 - AUPRC: 0.7339 - f1_score: 0.6144 - balanced_accuracy: 0.7413 - specificity: 0.9869 - miss_rate: 0.5044 - fall_out: 0.0131 - mcc: 0.6034 - val_loss: 1.0666 - val_accuracy: 0.6750 - val_recall: 0.5000 - val_precision: 0.7692 - val_AUROC: 0.9400 - val_AUPRC: 0.7160 - val_f1_score: 0.6061 - val_balanced_accuracy: 0.7417 - val_specificity: 0.9833 - val_miss_rate: 0.5000 - val_fall_out: 0.0167 - val_mcc: 0.5882
25/25 [==============================] - 0s 5ms/step - loss: 0.6872 - accuracy: 0.8073 - recall: 0.5982 - precision: 0.9282 - AUROC: 0.9810 - AUPRC: 0.8854 - f1_score: 0.7275 - balanced_accuracy: 0.7966 - specificity: 0.9949 - miss_rate: 0.4018 - fall_out: 0.0051 - mcc: 0.7246
7/7 [==============================] - 0s 5ms/step - loss: 1.0666 - accuracy: 0.6750 - recall: 0.5000 - precision: 0.7692 - AUROC: 0.9400 - AUPRC: 0.7160 - f1_score: 0.6061 - balanced_accuracy: 0.7417 - specificity: 0.9833 - miss_rate: 0.5000 - fall_out: 0.0167 - mcc: 0.5882
1it [00:09, 9.37s/it]
-- HOLDOUT 2 -- WINDOW window_30s
-- 21 Uncorrelated features: [Pearson+Spearman]
['mfcc16_mean', 'harmony_mean', 'mfcc10_var', 'mfcc4_mean', 'mfcc15_mean', 'mfcc19_mean', 'mfcc6_mean', 'mfcc8_var', 'mfcc9_var', 'mfcc3_mean', 'chroma_stft_var', 'mfcc3_var', 'mfcc1_var', 'mfcc2_var', 'mfcc7_var', 'tempo', 'mfcc13_mean', 'mfcc5_mean', 'mfcc17_mean', 'perceptr_mean', 'mfcc5_var']
-- Actually uncorrelated features: [confirmed via MIC]
[]
-- 1 Correlated features: [Pearson]
['spectral_centroid_mean']
Model: "MLP"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_184 (Dense) (None, 128) 7296
dropout_143 (Dropout) (None, 128) 0
dense_185 (Dense) (None, 64) 8256
dropout_144 (Dropout) (None, 64) 0
dense_186 (Dense) (None, 64) 4160
dropout_145 (Dropout) (None, 64) 0
dense_187 (Dense) (None, 10) 650
=================================================================
Total params: 20,362
Trainable params: 20,362
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
7/7 [==============================] - 2s 85ms/step - loss: 2.8221 - accuracy: 0.0951 - recall: 0.0163 - precision: 0.1857 - AUROC: 0.5170 - AUPRC: 0.1076 - f1_score: 0.0299 - balanced_accuracy: 0.5042 - specificity: 0.9921 - miss_rate: 0.9837 - fall_out: 0.0079 - mcc: 0.0269 - val_loss: 2.2708 - val_accuracy: 0.1300 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5612 - val_AUPRC: 0.1317 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 2/100
7/7 [==============================] - 0s 16ms/step - loss: 2.7745 - accuracy: 0.1364 - recall: 0.0050 - precision: 0.0889 - AUROC: 0.5343 - AUPRC: 0.1149 - f1_score: 0.0095 - balanced_accuracy: 0.4997 - specificity: 0.9943 - miss_rate: 0.9950 - fall_out: 0.0057 - mcc: -0.0028 - val_loss: 2.2255 - val_accuracy: 0.2000 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6250 - val_AUPRC: 0.1661 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 3/100
7/7 [==============================] - 0s 15ms/step - loss: 2.5703 - accuracy: 0.1289 - recall: 0.0063 - precision: 0.1389 - AUROC: 0.5499 - AUPRC: 0.1171 - f1_score: 0.0120 - balanced_accuracy: 0.5010 - specificity: 0.9957 - miss_rate: 0.9937 - fall_out: 0.0043 - mcc: 0.0087 - val_loss: 2.2038 - val_accuracy: 0.2400 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6758 - val_AUPRC: 0.1874 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 4/100
7/7 [==============================] - 0s 16ms/step - loss: 2.5427 - accuracy: 0.1339 - recall: 0.0063 - precision: 0.1515 - AUROC: 0.5608 - AUPRC: 0.1189 - f1_score: 0.0120 - balanced_accuracy: 0.5012 - specificity: 0.9961 - miss_rate: 0.9937 - fall_out: 0.0039 - mcc: 0.0111 - val_loss: 2.1871 - val_accuracy: 0.2850 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7117 - val_AUPRC: 0.2310 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 5/100
7/7 [==============================] - 0s 16ms/step - loss: 2.3813 - accuracy: 0.1477 - recall: 0.0088 - precision: 0.2121 - AUROC: 0.5843 - AUPRC: 0.1368 - f1_score: 0.0168 - balanced_accuracy: 0.5026 - specificity: 0.9964 - miss_rate: 0.9912 - fall_out: 0.0036 - mcc: 0.0241 - val_loss: 2.1640 - val_accuracy: 0.3400 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7367 - val_AUPRC: 0.2980 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 6/100
7/7 [==============================] - 0s 15ms/step - loss: 2.3695 - accuracy: 0.1840 - recall: 0.0063 - precision: 0.1923 - AUROC: 0.6071 - AUPRC: 0.1451 - f1_score: 0.0121 - balanced_accuracy: 0.5017 - specificity: 0.9971 - miss_rate: 0.9937 - fall_out: 0.0029 - mcc: 0.0176 - val_loss: 2.1408 - val_accuracy: 0.3400 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7545 - val_AUPRC: 0.3304 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 7/100
7/7 [==============================] - 0s 14ms/step - loss: 2.3833 - accuracy: 0.2228 - recall: 0.0113 - precision: 0.3750 - AUROC: 0.6321 - AUPRC: 0.1716 - f1_score: 0.0219 - balanced_accuracy: 0.5046 - specificity: 0.9979 - miss_rate: 0.9887 - fall_out: 0.0021 - mcc: 0.0503 - val_loss: 2.1129 - val_accuracy: 0.3600 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7739 - val_AUPRC: 0.3587 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 8/100
7/7 [==============================] - 0s 13ms/step - loss: 2.3160 - accuracy: 0.2153 - recall: 0.0063 - precision: 0.1724 - AUROC: 0.6400 - AUPRC: 0.1703 - f1_score: 0.0121 - balanced_accuracy: 0.5015 - specificity: 0.9967 - miss_rate: 0.9937 - fall_out: 0.0033 - mcc: 0.0146 - val_loss: 2.0828 - val_accuracy: 0.3450 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7885 - val_AUPRC: 0.3630 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 9/100
7/7 [==============================] - 0s 12ms/step - loss: 2.2500 - accuracy: 0.2390 - recall: 0.0100 - precision: 0.3200 - AUROC: 0.6490 - AUPRC: 0.1869 - f1_score: 0.0194 - balanced_accuracy: 0.5038 - specificity: 0.9976 - miss_rate: 0.9900 - fall_out: 0.0024 - mcc: 0.0411 - val_loss: 2.0422 - val_accuracy: 0.3600 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.8002 - val_AUPRC: 0.4069 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 10/100
7/7 [==============================] - 0s 12ms/step - loss: 2.2268 - accuracy: 0.2441 - recall: 0.0113 - precision: 0.2903 - AUROC: 0.6618 - AUPRC: 0.1973 - f1_score: 0.0217 - balanced_accuracy: 0.5041 - specificity: 0.9969 - miss_rate: 0.9887 - fall_out: 0.0031 - mcc: 0.0396 - val_loss: 2.0016 - val_accuracy: 0.3850 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.8123 - val_AUPRC: 0.4416 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 11/100
7/7 [==============================] - 0s 12ms/step - loss: 2.1838 - accuracy: 0.2703 - recall: 0.0263 - precision: 0.4773 - AUROC: 0.6994 - AUPRC: 0.2257 - f1_score: 0.0498 - balanced_accuracy: 0.5115 - specificity: 0.9968 - miss_rate: 0.9737 - fall_out: 0.0032 - mcc: 0.0936 - val_loss: 1.9526 - val_accuracy: 0.3800 - val_recall: 0.0150 - val_precision: 1.0000 - val_AUROC: 0.8227 - val_AUPRC: 0.4502 - val_f1_score: 0.0296 - val_balanced_accuracy: 0.5075 - val_specificity: 1.0000 - val_miss_rate: 0.9850 - val_fall_out: 0.0000e+00 - val_mcc: 0.1163
Epoch 12/100
7/7 [==============================] - 0s 13ms/step - loss: 2.1202 - accuracy: 0.2916 - recall: 0.0313 - precision: 0.5102 - AUROC: 0.7192 - AUPRC: 0.2558 - f1_score: 0.0590 - balanced_accuracy: 0.5140 - specificity: 0.9967 - miss_rate: 0.9687 - fall_out: 0.0033 - mcc: 0.1074 - val_loss: 1.9049 - val_accuracy: 0.4000 - val_recall: 0.0300 - val_precision: 0.8571 - val_AUROC: 0.8328 - val_AUPRC: 0.4572 - val_f1_score: 0.0580 - val_balanced_accuracy: 0.5147 - val_specificity: 0.9994 - val_miss_rate: 0.9700 - val_fall_out: 5.5556e-04 - val_mcc: 0.1496
Epoch 13/100
7/7 [==============================] - 0s 12ms/step - loss: 2.0449 - accuracy: 0.3066 - recall: 0.0576 - precision: 0.6571 - AUROC: 0.7372 - AUPRC: 0.2869 - f1_score: 0.1059 - balanced_accuracy: 0.5271 - specificity: 0.9967 - miss_rate: 0.9424 - fall_out: 0.0033 - mcc: 0.1746 - val_loss: 1.8503 - val_accuracy: 0.4200 - val_recall: 0.0400 - val_precision: 0.8889 - val_AUROC: 0.8404 - val_AUPRC: 0.4669 - val_f1_score: 0.0766 - val_balanced_accuracy: 0.5197 - val_specificity: 0.9994 - val_miss_rate: 0.9600 - val_fall_out: 5.5556e-04 - val_mcc: 0.1768
Epoch 14/100
7/7 [==============================] - 0s 12ms/step - loss: 2.0345 - accuracy: 0.3029 - recall: 0.0751 - precision: 0.6818 - AUROC: 0.7380 - AUPRC: 0.2916 - f1_score: 0.1353 - balanced_accuracy: 0.5356 - specificity: 0.9961 - miss_rate: 0.9249 - fall_out: 0.0039 - mcc: 0.2047 - val_loss: 1.8107 - val_accuracy: 0.4350 - val_recall: 0.0900 - val_precision: 0.9474 - val_AUROC: 0.8450 - val_AUPRC: 0.4718 - val_f1_score: 0.1644 - val_balanced_accuracy: 0.5447 - val_specificity: 0.9994 - val_miss_rate: 0.9100 - val_fall_out: 5.5556e-04 - val_mcc: 0.2766
Epoch 15/100
7/7 [==============================] - 0s 13ms/step - loss: 1.9873 - accuracy: 0.3104 - recall: 0.0864 - precision: 0.6970 - AUROC: 0.7673 - AUPRC: 0.3131 - f1_score: 0.1537 - balanced_accuracy: 0.5411 - specificity: 0.9958 - miss_rate: 0.9136 - fall_out: 0.0042 - mcc: 0.2229 - val_loss: 1.7722 - val_accuracy: 0.4500 - val_recall: 0.1000 - val_precision: 0.9091 - val_AUROC: 0.8511 - val_AUPRC: 0.4807 - val_f1_score: 0.1802 - val_balanced_accuracy: 0.5494 - val_specificity: 0.9989 - val_miss_rate: 0.9000 - val_fall_out: 0.0011 - val_mcc: 0.2844
Epoch 16/100
7/7 [==============================] - 0s 12ms/step - loss: 1.9712 - accuracy: 0.3267 - recall: 0.0914 - precision: 0.6348 - AUROC: 0.7679 - AUPRC: 0.3169 - f1_score: 0.1597 - balanced_accuracy: 0.5428 - specificity: 0.9942 - miss_rate: 0.9086 - fall_out: 0.0058 - mcc: 0.2154 - val_loss: 1.7351 - val_accuracy: 0.4350 - val_recall: 0.1200 - val_precision: 0.8571 - val_AUROC: 0.8578 - val_AUPRC: 0.4920 - val_f1_score: 0.2105 - val_balanced_accuracy: 0.5589 - val_specificity: 0.9978 - val_miss_rate: 0.8800 - val_fall_out: 0.0022 - val_mcc: 0.3007
Epoch 17/100
7/7 [==============================] - 0s 12ms/step - loss: 1.9835 - accuracy: 0.3342 - recall: 0.1101 - precision: 0.6667 - AUROC: 0.7682 - AUPRC: 0.3274 - f1_score: 0.1890 - balanced_accuracy: 0.5520 - specificity: 0.9939 - miss_rate: 0.8899 - fall_out: 0.0061 - mcc: 0.2448 - val_loss: 1.7063 - val_accuracy: 0.4300 - val_recall: 0.1550 - val_precision: 0.8611 - val_AUROC: 0.8666 - val_AUPRC: 0.5038 - val_f1_score: 0.2627 - val_balanced_accuracy: 0.5761 - val_specificity: 0.9972 - val_miss_rate: 0.8450 - val_fall_out: 0.0028 - val_mcc: 0.3435
Epoch 18/100
7/7 [==============================] - 0s 12ms/step - loss: 1.9736 - accuracy: 0.3367 - recall: 0.1064 - precision: 0.6343 - AUROC: 0.7735 - AUPRC: 0.3219 - f1_score: 0.1822 - balanced_accuracy: 0.5498 - specificity: 0.9932 - miss_rate: 0.8936 - fall_out: 0.0068 - mcc: 0.2326 - val_loss: 1.6813 - val_accuracy: 0.4500 - val_recall: 0.1700 - val_precision: 0.8718 - val_AUROC: 0.8708 - val_AUPRC: 0.5065 - val_f1_score: 0.2845 - val_balanced_accuracy: 0.5836 - val_specificity: 0.9972 - val_miss_rate: 0.8300 - val_fall_out: 0.0028 - val_mcc: 0.3628
Epoch 19/100
7/7 [==============================] - 0s 12ms/step - loss: 1.8321 - accuracy: 0.3529 - recall: 0.1151 - precision: 0.6301 - AUROC: 0.8013 - AUPRC: 0.3611 - f1_score: 0.1947 - balanced_accuracy: 0.5538 - specificity: 0.9925 - miss_rate: 0.8849 - fall_out: 0.0075 - mcc: 0.2411 - val_loss: 1.6539 - val_accuracy: 0.4300 - val_recall: 0.1750 - val_precision: 0.8974 - val_AUROC: 0.8760 - val_AUPRC: 0.5156 - val_f1_score: 0.2929 - val_balanced_accuracy: 0.5864 - val_specificity: 0.9978 - val_miss_rate: 0.8250 - val_fall_out: 0.0022 - val_mcc: 0.3749
Epoch 20/100
7/7 [==============================] - 0s 12ms/step - loss: 1.8550 - accuracy: 0.3705 - recall: 0.1302 - precision: 0.6710 - AUROC: 0.8085 - AUPRC: 0.3807 - f1_score: 0.2180 - balanced_accuracy: 0.5615 - specificity: 0.9929 - miss_rate: 0.8698 - fall_out: 0.0071 - mcc: 0.2677 - val_loss: 1.6239 - val_accuracy: 0.4350 - val_recall: 0.1850 - val_precision: 0.8810 - val_AUROC: 0.8813 - val_AUPRC: 0.5213 - val_f1_score: 0.3058 - val_balanced_accuracy: 0.5911 - val_specificity: 0.9972 - val_miss_rate: 0.8150 - val_fall_out: 0.0028 - val_mcc: 0.3813
Epoch 21/100
7/7 [==============================] - 0s 12ms/step - loss: 1.9149 - accuracy: 0.3492 - recall: 0.1339 - precision: 0.6149 - AUROC: 0.8011 - AUPRC: 0.3528 - f1_score: 0.2199 - balanced_accuracy: 0.5623 - specificity: 0.9907 - miss_rate: 0.8661 - fall_out: 0.0093 - mcc: 0.2561 - val_loss: 1.6107 - val_accuracy: 0.4500 - val_recall: 0.2000 - val_precision: 0.8696 - val_AUROC: 0.8852 - val_AUPRC: 0.5285 - val_f1_score: 0.3252 - val_balanced_accuracy: 0.5983 - val_specificity: 0.9967 - val_miss_rate: 0.8000 - val_fall_out: 0.0033 - val_mcc: 0.3936
Epoch 22/100
7/7 [==============================] - 0s 12ms/step - loss: 1.8080 - accuracy: 0.3992 - recall: 0.1314 - precision: 0.6213 - AUROC: 0.8179 - AUPRC: 0.3851 - f1_score: 0.2169 - balanced_accuracy: 0.5613 - specificity: 0.9911 - miss_rate: 0.8686 - fall_out: 0.0089 - mcc: 0.2554 - val_loss: 1.5901 - val_accuracy: 0.4400 - val_recall: 0.2050 - val_precision: 0.8723 - val_AUROC: 0.8868 - val_AUPRC: 0.5293 - val_f1_score: 0.3320 - val_balanced_accuracy: 0.6008 - val_specificity: 0.9967 - val_miss_rate: 0.7950 - val_fall_out: 0.0033 - val_mcc: 0.3994
Epoch 23/100
7/7 [==============================] - 0s 13ms/step - loss: 1.8012 - accuracy: 0.3967 - recall: 0.1464 - precision: 0.6324 - AUROC: 0.8197 - AUPRC: 0.3944 - f1_score: 0.2378 - balanced_accuracy: 0.5685 - specificity: 0.9905 - miss_rate: 0.8536 - fall_out: 0.0095 - mcc: 0.2732 - val_loss: 1.5744 - val_accuracy: 0.4500 - val_recall: 0.2050 - val_precision: 0.8723 - val_AUROC: 0.8885 - val_AUPRC: 0.5332 - val_f1_score: 0.3320 - val_balanced_accuracy: 0.6008 - val_specificity: 0.9967 - val_miss_rate: 0.7950 - val_fall_out: 0.0033 - val_mcc: 0.3994
Epoch 24/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7356 - accuracy: 0.3792 - recall: 0.1439 - precision: 0.6354 - AUROC: 0.8313 - AUPRC: 0.4019 - f1_score: 0.2347 - balanced_accuracy: 0.5674 - specificity: 0.9908 - miss_rate: 0.8561 - fall_out: 0.0092 - mcc: 0.2717 - val_loss: 1.5457 - val_accuracy: 0.4450 - val_recall: 0.2150 - val_precision: 0.8776 - val_AUROC: 0.8937 - val_AUPRC: 0.5439 - val_f1_score: 0.3454 - val_balanced_accuracy: 0.6058 - val_specificity: 0.9967 - val_miss_rate: 0.7850 - val_fall_out: 0.0033 - val_mcc: 0.4107
Epoch 25/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7023 - accuracy: 0.4093 - recall: 0.1790 - precision: 0.6471 - AUROC: 0.8309 - AUPRC: 0.4121 - f1_score: 0.2804 - balanced_accuracy: 0.5841 - specificity: 0.9892 - miss_rate: 0.8210 - fall_out: 0.0108 - mcc: 0.3076 - val_loss: 1.5300 - val_accuracy: 0.4650 - val_recall: 0.2100 - val_precision: 0.8571 - val_AUROC: 0.8959 - val_AUPRC: 0.5521 - val_f1_score: 0.3373 - val_balanced_accuracy: 0.6031 - val_specificity: 0.9961 - val_miss_rate: 0.7900 - val_fall_out: 0.0039 - val_mcc: 0.4000
Epoch 26/100
7/7 [==============================] - 0s 12ms/step - loss: 1.6730 - accuracy: 0.4093 - recall: 0.1827 - precision: 0.7087 - AUROC: 0.8380 - AUPRC: 0.4373 - f1_score: 0.2905 - balanced_accuracy: 0.5872 - specificity: 0.9917 - miss_rate: 0.8173 - fall_out: 0.0083 - mcc: 0.3301 - val_loss: 1.4980 - val_accuracy: 0.4700 - val_recall: 0.2200 - val_precision: 0.8627 - val_AUROC: 0.9009 - val_AUPRC: 0.5633 - val_f1_score: 0.3506 - val_balanced_accuracy: 0.6081 - val_specificity: 0.9961 - val_miss_rate: 0.7800 - val_fall_out: 0.0039 - val_mcc: 0.4113
Epoch 27/100
7/7 [==============================] - 0s 13ms/step - loss: 1.6668 - accuracy: 0.4205 - recall: 0.1715 - precision: 0.6618 - AUROC: 0.8375 - AUPRC: 0.4325 - f1_score: 0.2724 - balanced_accuracy: 0.5809 - specificity: 0.9903 - miss_rate: 0.8285 - fall_out: 0.0097 - mcc: 0.3054 - val_loss: 1.4677 - val_accuracy: 0.4900 - val_recall: 0.2350 - val_precision: 0.8545 - val_AUROC: 0.9067 - val_AUPRC: 0.5763 - val_f1_score: 0.3686 - val_balanced_accuracy: 0.6153 - val_specificity: 0.9956 - val_miss_rate: 0.7650 - val_fall_out: 0.0044 - val_mcc: 0.4229
Epoch 28/100
7/7 [==============================] - 0s 15ms/step - loss: 1.6151 - accuracy: 0.4243 - recall: 0.1915 - precision: 0.7018 - AUROC: 0.8548 - AUPRC: 0.4622 - f1_score: 0.3009 - balanced_accuracy: 0.5912 - specificity: 0.9910 - miss_rate: 0.8085 - fall_out: 0.0090 - mcc: 0.3360 - val_loss: 1.4438 - val_accuracy: 0.5050 - val_recall: 0.2400 - val_precision: 0.8727 - val_AUROC: 0.9105 - val_AUPRC: 0.5832 - val_f1_score: 0.3765 - val_balanced_accuracy: 0.6181 - val_specificity: 0.9961 - val_miss_rate: 0.7600 - val_fall_out: 0.0039 - val_mcc: 0.4331
Epoch 29/100
7/7 [==============================] - 0s 15ms/step - loss: 1.6268 - accuracy: 0.4368 - recall: 0.1940 - precision: 0.6858 - AUROC: 0.8519 - AUPRC: 0.4544 - f1_score: 0.3024 - balanced_accuracy: 0.5921 - specificity: 0.9901 - miss_rate: 0.8060 - fall_out: 0.0099 - mcc: 0.3332 - val_loss: 1.4170 - val_accuracy: 0.5050 - val_recall: 0.2500 - val_precision: 0.8772 - val_AUROC: 0.9121 - val_AUPRC: 0.5867 - val_f1_score: 0.3891 - val_balanced_accuracy: 0.6231 - val_specificity: 0.9961 - val_miss_rate: 0.7500 - val_fall_out: 0.0039 - val_mcc: 0.4437
Epoch 30/100
7/7 [==============================] - 0s 14ms/step - loss: 1.6365 - accuracy: 0.4105 - recall: 0.1752 - precision: 0.6452 - AUROC: 0.8541 - AUPRC: 0.4480 - f1_score: 0.2756 - balanced_accuracy: 0.5823 - specificity: 0.9893 - miss_rate: 0.8248 - fall_out: 0.0107 - mcc: 0.3036 - val_loss: 1.4026 - val_accuracy: 0.5100 - val_recall: 0.2550 - val_precision: 0.8793 - val_AUROC: 0.9132 - val_AUPRC: 0.5914 - val_f1_score: 0.3953 - val_balanced_accuracy: 0.6256 - val_specificity: 0.9961 - val_miss_rate: 0.7450 - val_fall_out: 0.0039 - val_mcc: 0.4489
Epoch 31/100
7/7 [==============================] - 0s 14ms/step - loss: 1.5896 - accuracy: 0.4105 - recall: 0.2128 - precision: 0.6391 - AUROC: 0.8568 - AUPRC: 0.4542 - f1_score: 0.3192 - balanced_accuracy: 0.5997 - specificity: 0.9866 - miss_rate: 0.7872 - fall_out: 0.0134 - mcc: 0.3335 - val_loss: 1.3853 - val_accuracy: 0.5100 - val_recall: 0.2550 - val_precision: 0.8793 - val_AUROC: 0.9155 - val_AUPRC: 0.6008 - val_f1_score: 0.3953 - val_balanced_accuracy: 0.6256 - val_specificity: 0.9961 - val_miss_rate: 0.7450 - val_fall_out: 0.0039 - val_mcc: 0.4489
Epoch 32/100
7/7 [==============================] - 0s 13ms/step - loss: 1.5641 - accuracy: 0.4355 - recall: 0.2115 - precision: 0.6550 - AUROC: 0.8626 - AUPRC: 0.4832 - f1_score: 0.3198 - balanced_accuracy: 0.5996 - specificity: 0.9876 - miss_rate: 0.7885 - fall_out: 0.0124 - mcc: 0.3380 - val_loss: 1.3685 - val_accuracy: 0.5350 - val_recall: 0.2650 - val_precision: 0.8548 - val_AUROC: 0.9177 - val_AUPRC: 0.6100 - val_f1_score: 0.4046 - val_balanced_accuracy: 0.6300 - val_specificity: 0.9950 - val_miss_rate: 0.7350 - val_fall_out: 0.0050 - val_mcc: 0.4500
Epoch 33/100
7/7 [==============================] - 0s 13ms/step - loss: 1.5902 - accuracy: 0.4393 - recall: 0.2090 - precision: 0.6653 - AUROC: 0.8570 - AUPRC: 0.4664 - f1_score: 0.3181 - balanced_accuracy: 0.5987 - specificity: 0.9883 - miss_rate: 0.7910 - fall_out: 0.0117 - mcc: 0.3394 - val_loss: 1.3567 - val_accuracy: 0.5300 - val_recall: 0.2600 - val_precision: 0.8525 - val_AUROC: 0.9181 - val_AUPRC: 0.6104 - val_f1_score: 0.3985 - val_balanced_accuracy: 0.6275 - val_specificity: 0.9950 - val_miss_rate: 0.7400 - val_fall_out: 0.0050 - val_mcc: 0.4449
Epoch 34/100
7/7 [==============================] - 0s 12ms/step - loss: 1.6492 - accuracy: 0.4118 - recall: 0.2140 - precision: 0.6357 - AUROC: 0.8579 - AUPRC: 0.4476 - f1_score: 0.3202 - balanced_accuracy: 0.6002 - specificity: 0.9864 - miss_rate: 0.7860 - fall_out: 0.0136 - mcc: 0.3333 - val_loss: 1.3415 - val_accuracy: 0.5400 - val_recall: 0.2650 - val_precision: 0.8548 - val_AUROC: 0.9211 - val_AUPRC: 0.6214 - val_f1_score: 0.4046 - val_balanced_accuracy: 0.6300 - val_specificity: 0.9950 - val_miss_rate: 0.7350 - val_fall_out: 0.0050 - val_mcc: 0.4500
Epoch 35/100
7/7 [==============================] - 0s 11ms/step - loss: 1.5593 - accuracy: 0.4543 - recall: 0.2378 - precision: 0.6960 - AUROC: 0.8617 - AUPRC: 0.4861 - f1_score: 0.3545 - balanced_accuracy: 0.6131 - specificity: 0.9885 - miss_rate: 0.7622 - fall_out: 0.0115 - mcc: 0.3736 - val_loss: 1.3158 - val_accuracy: 0.5600 - val_recall: 0.2700 - val_precision: 0.8710 - val_AUROC: 0.9237 - val_AUPRC: 0.6305 - val_f1_score: 0.4122 - val_balanced_accuracy: 0.6328 - val_specificity: 0.9956 - val_miss_rate: 0.7300 - val_fall_out: 0.0044 - val_mcc: 0.4597
Epoch 36/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4863 - accuracy: 0.4906 - recall: 0.2453 - precision: 0.6829 - AUROC: 0.8826 - AUPRC: 0.5204 - f1_score: 0.3610 - balanced_accuracy: 0.6163 - specificity: 0.9873 - miss_rate: 0.7547 - fall_out: 0.0127 - mcc: 0.3751 - val_loss: 1.3064 - val_accuracy: 0.5500 - val_recall: 0.2750 - val_precision: 0.8594 - val_AUROC: 0.9232 - val_AUPRC: 0.6256 - val_f1_score: 0.4167 - val_balanced_accuracy: 0.6350 - val_specificity: 0.9950 - val_miss_rate: 0.7250 - val_fall_out: 0.0050 - val_mcc: 0.4602
Epoch 37/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4973 - accuracy: 0.4681 - recall: 0.2428 - precision: 0.6783 - AUROC: 0.8806 - AUPRC: 0.5127 - f1_score: 0.3576 - balanced_accuracy: 0.6150 - specificity: 0.9872 - miss_rate: 0.7572 - fall_out: 0.0128 - mcc: 0.3714 - val_loss: 1.2995 - val_accuracy: 0.5300 - val_recall: 0.2800 - val_precision: 0.8358 - val_AUROC: 0.9226 - val_AUPRC: 0.6218 - val_f1_score: 0.4195 - val_balanced_accuracy: 0.6369 - val_specificity: 0.9939 - val_miss_rate: 0.7200 - val_fall_out: 0.0061 - val_mcc: 0.4566
Epoch 38/100
7/7 [==============================] - 0s 13ms/step - loss: 1.5244 - accuracy: 0.4706 - recall: 0.2566 - precision: 0.6833 - AUROC: 0.8773 - AUPRC: 0.5179 - f1_score: 0.3731 - balanced_accuracy: 0.6217 - specificity: 0.9868 - miss_rate: 0.7434 - fall_out: 0.0132 - mcc: 0.3841 - val_loss: 1.2985 - val_accuracy: 0.5450 - val_recall: 0.2750 - val_precision: 0.8333 - val_AUROC: 0.9226 - val_AUPRC: 0.6229 - val_f1_score: 0.4135 - val_balanced_accuracy: 0.6344 - val_specificity: 0.9939 - val_miss_rate: 0.7250 - val_fall_out: 0.0061 - val_mcc: 0.4516
Epoch 39/100
7/7 [==============================] - 0s 14ms/step - loss: 1.4646 - accuracy: 0.4844 - recall: 0.2703 - precision: 0.6968 - AUROC: 0.8779 - AUPRC: 0.5315 - f1_score: 0.3895 - balanced_accuracy: 0.6286 - specificity: 0.9869 - miss_rate: 0.7297 - fall_out: 0.0131 - mcc: 0.3997 - val_loss: 1.2803 - val_accuracy: 0.5600 - val_recall: 0.2750 - val_precision: 0.8462 - val_AUROC: 0.9252 - val_AUPRC: 0.6334 - val_f1_score: 0.4151 - val_balanced_accuracy: 0.6347 - val_specificity: 0.9944 - val_miss_rate: 0.7250 - val_fall_out: 0.0056 - val_mcc: 0.4559
Epoch 40/100
7/7 [==============================] - 0s 13ms/step - loss: 1.4598 - accuracy: 0.4844 - recall: 0.2641 - precision: 0.7104 - AUROC: 0.8788 - AUPRC: 0.5286 - f1_score: 0.3850 - balanced_accuracy: 0.6261 - specificity: 0.9880 - miss_rate: 0.7359 - fall_out: 0.0120 - mcc: 0.3998 - val_loss: 1.2623 - val_accuracy: 0.5600 - val_recall: 0.2850 - val_precision: 0.8507 - val_AUROC: 0.9272 - val_AUPRC: 0.6427 - val_f1_score: 0.4270 - val_balanced_accuracy: 0.6397 - val_specificity: 0.9944 - val_miss_rate: 0.7150 - val_fall_out: 0.0056 - val_mcc: 0.4659
Epoch 41/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4552 - accuracy: 0.4894 - recall: 0.2666 - precision: 0.7245 - AUROC: 0.8849 - AUPRC: 0.5411 - f1_score: 0.3898 - balanced_accuracy: 0.6277 - specificity: 0.9887 - miss_rate: 0.7334 - fall_out: 0.0113 - mcc: 0.4069 - val_loss: 1.2368 - val_accuracy: 0.5550 - val_recall: 0.2900 - val_precision: 0.8657 - val_AUROC: 0.9306 - val_AUPRC: 0.6523 - val_f1_score: 0.4345 - val_balanced_accuracy: 0.6425 - val_specificity: 0.9950 - val_miss_rate: 0.7100 - val_fall_out: 0.0050 - val_mcc: 0.4752
Epoch 42/100
7/7 [==============================] - 0s 13ms/step - loss: 1.4160 - accuracy: 0.5069 - recall: 0.2829 - precision: 0.6933 - AUROC: 0.8884 - AUPRC: 0.5268 - f1_score: 0.4018 - balanced_accuracy: 0.6345 - specificity: 0.9861 - miss_rate: 0.7171 - fall_out: 0.0139 - mcc: 0.4078 - val_loss: 1.2307 - val_accuracy: 0.5700 - val_recall: 0.2850 - val_precision: 0.8507 - val_AUROC: 0.9295 - val_AUPRC: 0.6545 - val_f1_score: 0.4270 - val_balanced_accuracy: 0.6397 - val_specificity: 0.9944 - val_miss_rate: 0.7150 - val_fall_out: 0.0056 - val_mcc: 0.4659
Epoch 43/100
7/7 [==============================] - 0s 13ms/step - loss: 1.4366 - accuracy: 0.5094 - recall: 0.2628 - precision: 0.6954 - AUROC: 0.8891 - AUPRC: 0.5415 - f1_score: 0.3815 - balanced_accuracy: 0.6250 - specificity: 0.9872 - miss_rate: 0.7372 - fall_out: 0.0128 - mcc: 0.3933 - val_loss: 1.2182 - val_accuracy: 0.5850 - val_recall: 0.2900 - val_precision: 0.8406 - val_AUROC: 0.9302 - val_AUPRC: 0.6604 - val_f1_score: 0.4312 - val_balanced_accuracy: 0.6419 - val_specificity: 0.9939 - val_miss_rate: 0.7100 - val_fall_out: 0.0061 - val_mcc: 0.4666
Epoch 44/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3804 - accuracy: 0.5269 - recall: 0.2954 - precision: 0.7217 - AUROC: 0.8957 - AUPRC: 0.5668 - f1_score: 0.4192 - balanced_accuracy: 0.6414 - specificity: 0.9873 - miss_rate: 0.7046 - fall_out: 0.0127 - mcc: 0.4281 - val_loss: 1.2085 - val_accuracy: 0.5850 - val_recall: 0.3100 - val_precision: 0.8378 - val_AUROC: 0.9308 - val_AUPRC: 0.6592 - val_f1_score: 0.4526 - val_balanced_accuracy: 0.6517 - val_specificity: 0.9933 - val_miss_rate: 0.6900 - val_fall_out: 0.0067 - val_mcc: 0.4821
Epoch 45/100
7/7 [==============================] - 0s 13ms/step - loss: 1.3680 - accuracy: 0.5081 - recall: 0.2979 - precision: 0.7126 - AUROC: 0.8949 - AUPRC: 0.5626 - f1_score: 0.4201 - balanced_accuracy: 0.6423 - specificity: 0.9866 - miss_rate: 0.7021 - fall_out: 0.0134 - mcc: 0.4265 - val_loss: 1.1950 - val_accuracy: 0.5800 - val_recall: 0.3200 - val_precision: 0.8533 - val_AUROC: 0.9323 - val_AUPRC: 0.6676 - val_f1_score: 0.4655 - val_balanced_accuracy: 0.6569 - val_specificity: 0.9939 - val_miss_rate: 0.6800 - val_fall_out: 0.0061 - val_mcc: 0.4957
Epoch 46/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3373 - accuracy: 0.5207 - recall: 0.3166 - precision: 0.7312 - AUROC: 0.9014 - AUPRC: 0.5907 - f1_score: 0.4419 - balanced_accuracy: 0.6519 - specificity: 0.9871 - miss_rate: 0.6834 - fall_out: 0.0129 - mcc: 0.4476 - val_loss: 1.1830 - val_accuracy: 0.5950 - val_recall: 0.3400 - val_precision: 0.8608 - val_AUROC: 0.9328 - val_AUPRC: 0.6725 - val_f1_score: 0.4875 - val_balanced_accuracy: 0.6669 - val_specificity: 0.9939 - val_miss_rate: 0.6600 - val_fall_out: 0.0061 - val_mcc: 0.5143
Epoch 47/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3256 - accuracy: 0.5181 - recall: 0.3129 - precision: 0.6812 - AUROC: 0.9031 - AUPRC: 0.5696 - f1_score: 0.4288 - balanced_accuracy: 0.6483 - specificity: 0.9837 - miss_rate: 0.6871 - fall_out: 0.0163 - mcc: 0.4251 - val_loss: 1.1731 - val_accuracy: 0.5950 - val_recall: 0.3500 - val_precision: 0.8434 - val_AUROC: 0.9329 - val_AUPRC: 0.6742 - val_f1_score: 0.4947 - val_balanced_accuracy: 0.6714 - val_specificity: 0.9928 - val_miss_rate: 0.6500 - val_fall_out: 0.0072 - val_mcc: 0.5156
Epoch 48/100
7/7 [==============================] - 0s 13ms/step - loss: 1.3208 - accuracy: 0.5282 - recall: 0.3292 - precision: 0.7367 - AUROC: 0.9047 - AUPRC: 0.5876 - f1_score: 0.4550 - balanced_accuracy: 0.6580 - specificity: 0.9869 - miss_rate: 0.6708 - fall_out: 0.0131 - mcc: 0.4590 - val_loss: 1.1608 - val_accuracy: 0.5900 - val_recall: 0.3600 - val_precision: 0.8471 - val_AUROC: 0.9342 - val_AUPRC: 0.6782 - val_f1_score: 0.5053 - val_balanced_accuracy: 0.6764 - val_specificity: 0.9928 - val_miss_rate: 0.6400 - val_fall_out: 0.0072 - val_mcc: 0.5246
Epoch 49/100
7/7 [==============================] - 0s 14ms/step - loss: 1.3221 - accuracy: 0.5544 - recall: 0.3179 - precision: 0.6959 - AUROC: 0.9055 - AUPRC: 0.5832 - f1_score: 0.4364 - balanced_accuracy: 0.6512 - specificity: 0.9846 - miss_rate: 0.6821 - fall_out: 0.0154 - mcc: 0.4346 - val_loss: 1.1505 - val_accuracy: 0.5850 - val_recall: 0.3500 - val_precision: 0.8333 - val_AUROC: 0.9347 - val_AUPRC: 0.6844 - val_f1_score: 0.4930 - val_balanced_accuracy: 0.6711 - val_specificity: 0.9922 - val_miss_rate: 0.6500 - val_fall_out: 0.0078 - val_mcc: 0.5118
Epoch 50/100
7/7 [==============================] - 0s 14ms/step - loss: 1.3437 - accuracy: 0.5094 - recall: 0.3279 - precision: 0.6823 - AUROC: 0.8996 - AUPRC: 0.5638 - f1_score: 0.4429 - balanced_accuracy: 0.6555 - specificity: 0.9830 - miss_rate: 0.6721 - fall_out: 0.0170 - mcc: 0.4361 - val_loss: 1.1467 - val_accuracy: 0.6150 - val_recall: 0.3550 - val_precision: 0.8353 - val_AUROC: 0.9346 - val_AUPRC: 0.6866 - val_f1_score: 0.4982 - val_balanced_accuracy: 0.6736 - val_specificity: 0.9922 - val_miss_rate: 0.6450 - val_fall_out: 0.0078 - val_mcc: 0.5164
Epoch 51/100
7/7 [==============================] - 0s 14ms/step - loss: 1.3197 - accuracy: 0.5357 - recall: 0.3179 - precision: 0.6997 - AUROC: 0.9063 - AUPRC: 0.5749 - f1_score: 0.4372 - balanced_accuracy: 0.6514 - specificity: 0.9848 - miss_rate: 0.6821 - fall_out: 0.0152 - mcc: 0.4361 - val_loss: 1.1416 - val_accuracy: 0.6100 - val_recall: 0.3650 - val_precision: 0.8488 - val_AUROC: 0.9358 - val_AUPRC: 0.6901 - val_f1_score: 0.5105 - val_balanced_accuracy: 0.6789 - val_specificity: 0.9928 - val_miss_rate: 0.6350 - val_fall_out: 0.0072 - val_mcc: 0.5291
Epoch 52/100
7/7 [==============================] - 0s 14ms/step - loss: 1.3470 - accuracy: 0.5469 - recall: 0.3492 - precision: 0.7520 - AUROC: 0.8997 - AUPRC: 0.5934 - f1_score: 0.4769 - balanced_accuracy: 0.6682 - specificity: 0.9872 - miss_rate: 0.6508 - fall_out: 0.0128 - mcc: 0.4796 - val_loss: 1.1363 - val_accuracy: 0.5900 - val_recall: 0.3650 - val_precision: 0.8488 - val_AUROC: 0.9367 - val_AUPRC: 0.6919 - val_f1_score: 0.5105 - val_balanced_accuracy: 0.6789 - val_specificity: 0.9928 - val_miss_rate: 0.6350 - val_fall_out: 0.0072 - val_mcc: 0.5291
Epoch 53/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2770 - accuracy: 0.5469 - recall: 0.3317 - precision: 0.7067 - AUROC: 0.9101 - AUPRC: 0.5987 - f1_score: 0.4514 - balanced_accuracy: 0.6582 - specificity: 0.9847 - miss_rate: 0.6683 - fall_out: 0.0153 - mcc: 0.4488 - val_loss: 1.1433 - val_accuracy: 0.5900 - val_recall: 0.3600 - val_precision: 0.8182 - val_AUROC: 0.9357 - val_AUPRC: 0.6829 - val_f1_score: 0.5000 - val_balanced_accuracy: 0.6756 - val_specificity: 0.9911 - val_miss_rate: 0.6400 - val_fall_out: 0.0089 - val_mcc: 0.5136
Epoch 54/100
7/7 [==============================] - 0s 13ms/step - loss: 1.2224 - accuracy: 0.5444 - recall: 0.3317 - precision: 0.7528 - AUROC: 0.9184 - AUPRC: 0.6220 - f1_score: 0.4605 - balanced_accuracy: 0.6598 - specificity: 0.9879 - miss_rate: 0.6683 - fall_out: 0.0121 - mcc: 0.4672 - val_loss: 1.1326 - val_accuracy: 0.6000 - val_recall: 0.3800 - val_precision: 0.8261 - val_AUROC: 0.9359 - val_AUPRC: 0.6880 - val_f1_score: 0.5205 - val_balanced_accuracy: 0.6856 - val_specificity: 0.9911 - val_miss_rate: 0.6200 - val_fall_out: 0.0089 - val_mcc: 0.5315
Epoch 55/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2763 - accuracy: 0.5732 - recall: 0.3579 - precision: 0.7526 - AUROC: 0.9110 - AUPRC: 0.6058 - f1_score: 0.4852 - balanced_accuracy: 0.6724 - specificity: 0.9869 - miss_rate: 0.6421 - fall_out: 0.0131 - mcc: 0.4861 - val_loss: 1.1198 - val_accuracy: 0.6250 - val_recall: 0.3900 - val_precision: 0.8211 - val_AUROC: 0.9364 - val_AUPRC: 0.6939 - val_f1_score: 0.5288 - val_balanced_accuracy: 0.6903 - val_specificity: 0.9906 - val_miss_rate: 0.6100 - val_fall_out: 0.0094 - val_mcc: 0.5367
Epoch 56/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2351 - accuracy: 0.5494 - recall: 0.3592 - precision: 0.7303 - AUROC: 0.9168 - AUPRC: 0.6273 - f1_score: 0.4815 - balanced_accuracy: 0.6722 - specificity: 0.9853 - miss_rate: 0.6408 - fall_out: 0.0147 - mcc: 0.4778 - val_loss: 1.1161 - val_accuracy: 0.6000 - val_recall: 0.3950 - val_precision: 0.8061 - val_AUROC: 0.9357 - val_AUPRC: 0.6915 - val_f1_score: 0.5302 - val_balanced_accuracy: 0.6922 - val_specificity: 0.9894 - val_miss_rate: 0.6050 - val_fall_out: 0.0106 - val_mcc: 0.5343
Epoch 57/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2515 - accuracy: 0.5707 - recall: 0.3692 - precision: 0.7178 - AUROC: 0.9152 - AUPRC: 0.6108 - f1_score: 0.4876 - balanced_accuracy: 0.6765 - specificity: 0.9839 - miss_rate: 0.6308 - fall_out: 0.0161 - mcc: 0.4795 - val_loss: 1.1003 - val_accuracy: 0.6150 - val_recall: 0.4150 - val_precision: 0.8300 - val_AUROC: 0.9379 - val_AUPRC: 0.6996 - val_f1_score: 0.5533 - val_balanced_accuracy: 0.7028 - val_specificity: 0.9906 - val_miss_rate: 0.5850 - val_fall_out: 0.0094 - val_mcc: 0.5582
Epoch 58/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2387 - accuracy: 0.5707 - recall: 0.3780 - precision: 0.7457 - AUROC: 0.9141 - AUPRC: 0.6249 - f1_score: 0.5017 - balanced_accuracy: 0.6818 - specificity: 0.9857 - miss_rate: 0.6220 - fall_out: 0.0143 - mcc: 0.4973 - val_loss: 1.0984 - val_accuracy: 0.6150 - val_recall: 0.4000 - val_precision: 0.8247 - val_AUROC: 0.9386 - val_AUPRC: 0.7015 - val_f1_score: 0.5387 - val_balanced_accuracy: 0.6953 - val_specificity: 0.9906 - val_miss_rate: 0.6000 - val_fall_out: 0.0094 - val_mcc: 0.5454
Epoch 59/100
7/7 [==============================] - 0s 13ms/step - loss: 1.2345 - accuracy: 0.5645 - recall: 0.3554 - precision: 0.7493 - AUROC: 0.9188 - AUPRC: 0.6298 - f1_score: 0.4822 - balanced_accuracy: 0.6711 - specificity: 0.9868 - miss_rate: 0.6446 - fall_out: 0.0132 - mcc: 0.4830 - val_loss: 1.0902 - val_accuracy: 0.6100 - val_recall: 0.4000 - val_precision: 0.8247 - val_AUROC: 0.9392 - val_AUPRC: 0.7036 - val_f1_score: 0.5387 - val_balanced_accuracy: 0.6953 - val_specificity: 0.9906 - val_miss_rate: 0.6000 - val_fall_out: 0.0094 - val_mcc: 0.5454
Epoch 60/100
7/7 [==============================] - 0s 13ms/step - loss: 1.2342 - accuracy: 0.5970 - recall: 0.3905 - precision: 0.7464 - AUROC: 0.9176 - AUPRC: 0.6372 - f1_score: 0.5127 - balanced_accuracy: 0.6879 - specificity: 0.9853 - miss_rate: 0.6095 - fall_out: 0.0147 - mcc: 0.5063 - val_loss: 1.0856 - val_accuracy: 0.6150 - val_recall: 0.4100 - val_precision: 0.8283 - val_AUROC: 0.9393 - val_AUPRC: 0.7051 - val_f1_score: 0.5485 - val_balanced_accuracy: 0.7003 - val_specificity: 0.9906 - val_miss_rate: 0.5900 - val_fall_out: 0.0094 - val_mcc: 0.5540
Epoch 61/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1556 - accuracy: 0.6133 - recall: 0.3880 - precision: 0.7928 - AUROC: 0.9301 - AUPRC: 0.6679 - f1_score: 0.5210 - balanced_accuracy: 0.6884 - specificity: 0.9887 - miss_rate: 0.6120 - fall_out: 0.0113 - mcc: 0.5239 - val_loss: 1.0799 - val_accuracy: 0.6100 - val_recall: 0.4200 - val_precision: 0.8235 - val_AUROC: 0.9393 - val_AUPRC: 0.7053 - val_f1_score: 0.5563 - val_balanced_accuracy: 0.7050 - val_specificity: 0.9900 - val_miss_rate: 0.5800 - val_fall_out: 0.0100 - val_mcc: 0.5591
Epoch 62/100
7/7 [==============================] - 0s 13ms/step - loss: 1.1789 - accuracy: 0.5870 - recall: 0.3817 - precision: 0.7439 - AUROC: 0.9227 - AUPRC: 0.6528 - f1_score: 0.5045 - balanced_accuracy: 0.6836 - specificity: 0.9854 - miss_rate: 0.6183 - fall_out: 0.0146 - mcc: 0.4992 - val_loss: 1.0691 - val_accuracy: 0.6150 - val_recall: 0.4400 - val_precision: 0.8302 - val_AUROC: 0.9401 - val_AUPRC: 0.7120 - val_f1_score: 0.5752 - val_balanced_accuracy: 0.7150 - val_specificity: 0.9900 - val_miss_rate: 0.5600 - val_fall_out: 0.0100 - val_mcc: 0.5758
Epoch 63/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1487 - accuracy: 0.6095 - recall: 0.4043 - precision: 0.7783 - AUROC: 0.9304 - AUPRC: 0.6623 - f1_score: 0.5321 - balanced_accuracy: 0.6957 - specificity: 0.9872 - miss_rate: 0.5957 - fall_out: 0.0128 - mcc: 0.5292 - val_loss: 1.0592 - val_accuracy: 0.6400 - val_recall: 0.4400 - val_precision: 0.8462 - val_AUROC: 0.9412 - val_AUPRC: 0.7169 - val_f1_score: 0.5789 - val_balanced_accuracy: 0.7156 - val_specificity: 0.9911 - val_miss_rate: 0.5600 - val_fall_out: 0.0089 - val_mcc: 0.5825
Epoch 64/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1095 - accuracy: 0.5932 - recall: 0.3967 - precision: 0.7694 - AUROC: 0.9344 - AUPRC: 0.6749 - f1_score: 0.5235 - balanced_accuracy: 0.6918 - specificity: 0.9868 - miss_rate: 0.6033 - fall_out: 0.0132 - mcc: 0.5203 - val_loss: 1.0415 - val_accuracy: 0.6300 - val_recall: 0.4600 - val_precision: 0.8440 - val_AUROC: 0.9429 - val_AUPRC: 0.7239 - val_f1_score: 0.5955 - val_balanced_accuracy: 0.7253 - val_specificity: 0.9906 - val_miss_rate: 0.5400 - val_fall_out: 0.0094 - val_mcc: 0.5954
Epoch 65/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1203 - accuracy: 0.6095 - recall: 0.4093 - precision: 0.7483 - AUROC: 0.9319 - AUPRC: 0.6691 - f1_score: 0.5291 - balanced_accuracy: 0.6970 - specificity: 0.9847 - miss_rate: 0.5907 - fall_out: 0.0153 - mcc: 0.5198 - val_loss: 1.0401 - val_accuracy: 0.6400 - val_recall: 0.4650 - val_precision: 0.8304 - val_AUROC: 0.9424 - val_AUPRC: 0.7232 - val_f1_score: 0.5962 - val_balanced_accuracy: 0.7272 - val_specificity: 0.9894 - val_miss_rate: 0.5350 - val_fall_out: 0.0106 - val_mcc: 0.5930
Epoch 66/100
7/7 [==============================] - 0s 12ms/step - loss: 1.0961 - accuracy: 0.6258 - recall: 0.4318 - precision: 0.7913 - AUROC: 0.9347 - AUPRC: 0.6852 - f1_score: 0.5587 - balanced_accuracy: 0.7096 - specificity: 0.9873 - miss_rate: 0.5682 - fall_out: 0.0127 - mcc: 0.5536 - val_loss: 1.0325 - val_accuracy: 0.6400 - val_recall: 0.4700 - val_precision: 0.8393 - val_AUROC: 0.9422 - val_AUPRC: 0.7275 - val_f1_score: 0.6026 - val_balanced_accuracy: 0.7300 - val_specificity: 0.9900 - val_miss_rate: 0.5300 - val_fall_out: 0.0100 - val_mcc: 0.6002
Epoch 67/100
7/7 [==============================] - 0s 12ms/step - loss: 1.0650 - accuracy: 0.6195 - recall: 0.4456 - precision: 0.7756 - AUROC: 0.9396 - AUPRC: 0.7025 - f1_score: 0.5660 - balanced_accuracy: 0.7156 - specificity: 0.9857 - miss_rate: 0.5544 - fall_out: 0.0143 - mcc: 0.5560 - val_loss: 1.0331 - val_accuracy: 0.6500 - val_recall: 0.4800 - val_precision: 0.8205 - val_AUROC: 0.9419 - val_AUPRC: 0.7249 - val_f1_score: 0.6057 - val_balanced_accuracy: 0.7342 - val_specificity: 0.9883 - val_miss_rate: 0.5200 - val_fall_out: 0.0117 - val_mcc: 0.5987
Epoch 68/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1392 - accuracy: 0.6170 - recall: 0.4318 - precision: 0.7468 - AUROC: 0.9289 - AUPRC: 0.6690 - f1_score: 0.5472 - balanced_accuracy: 0.7078 - specificity: 0.9837 - miss_rate: 0.5682 - fall_out: 0.0163 - mcc: 0.5341 - val_loss: 1.0254 - val_accuracy: 0.6500 - val_recall: 0.4700 - val_precision: 0.8393 - val_AUROC: 0.9423 - val_AUPRC: 0.7264 - val_f1_score: 0.6026 - val_balanced_accuracy: 0.7300 - val_specificity: 0.9900 - val_miss_rate: 0.5300 - val_fall_out: 0.0100 - val_mcc: 0.6002
Epoch 69/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1240 - accuracy: 0.6320 - recall: 0.4380 - precision: 0.7692 - AUROC: 0.9328 - AUPRC: 0.6871 - f1_score: 0.5582 - balanced_accuracy: 0.7117 - specificity: 0.9854 - miss_rate: 0.5620 - fall_out: 0.0146 - mcc: 0.5482 - val_loss: 1.0214 - val_accuracy: 0.6450 - val_recall: 0.4750 - val_precision: 0.8261 - val_AUROC: 0.9429 - val_AUPRC: 0.7290 - val_f1_score: 0.6032 - val_balanced_accuracy: 0.7319 - val_specificity: 0.9889 - val_miss_rate: 0.5250 - val_fall_out: 0.0111 - val_mcc: 0.5978
Epoch 70/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1240 - accuracy: 0.6083 - recall: 0.4255 - precision: 0.7522 - AUROC: 0.9317 - AUPRC: 0.6705 - f1_score: 0.5436 - balanced_accuracy: 0.7050 - specificity: 0.9844 - miss_rate: 0.5745 - fall_out: 0.0156 - mcc: 0.5324 - val_loss: 1.0222 - val_accuracy: 0.6500 - val_recall: 0.4650 - val_precision: 0.8378 - val_AUROC: 0.9429 - val_AUPRC: 0.7286 - val_f1_score: 0.5981 - val_balanced_accuracy: 0.7275 - val_specificity: 0.9900 - val_miss_rate: 0.5350 - val_fall_out: 0.0100 - val_mcc: 0.5962
Epoch 71/100
7/7 [==============================] - 0s 12ms/step - loss: 1.0966 - accuracy: 0.6320 - recall: 0.4493 - precision: 0.7838 - AUROC: 0.9336 - AUPRC: 0.7088 - f1_score: 0.5712 - balanced_accuracy: 0.7178 - specificity: 0.9862 - miss_rate: 0.5507 - fall_out: 0.0138 - mcc: 0.5621 - val_loss: 1.0234 - val_accuracy: 0.6550 - val_recall: 0.4700 - val_precision: 0.8319 - val_AUROC: 0.9420 - val_AUPRC: 0.7281 - val_f1_score: 0.6006 - val_balanced_accuracy: 0.7297 - val_specificity: 0.9894 - val_miss_rate: 0.5300 - val_fall_out: 0.0106 - val_mcc: 0.5970
25/25 [==============================] - 0s 5ms/step - loss: 0.7290 - accuracy: 0.7810 - recall: 0.5720 - precision: 0.8926 - AUROC: 0.9771 - AUPRC: 0.8648 - f1_score: 0.6972 - balanced_accuracy: 0.7822 - specificity: 0.9924 - miss_rate: 0.4280 - fall_out: 0.0076 - mcc: 0.6913
7/7 [==============================] - 0s 6ms/step - loss: 1.0234 - accuracy: 0.6550 - recall: 0.4700 - precision: 0.8319 - AUROC: 0.9420 - AUPRC: 0.7281 - f1_score: 0.6006 - balanced_accuracy: 0.7297 - specificity: 0.9894 - miss_rate: 0.5300 - fall_out: 0.0106 - mcc: 0.5970
2it [00:18, 9.06s/it]
-- HOLDOUT 3 -- WINDOW window_30s
-- 23 Uncorrelated features: [Pearson+Spearman]
['mfcc16_mean', 'harmony_mean', 'mfcc10_var', 'mfcc4_mean', 'mfcc15_mean', 'mfcc19_mean', 'mfcc6_mean', 'mfcc8_var', 'mfcc9_var', 'mfcc20_mean', 'mfcc3_mean', 'chroma_stft_var', 'mfcc3_var', 'mfcc1_var', 'mfcc2_var', 'mfcc7_var', 'tempo', 'mfcc13_mean', 'mfcc5_mean', 'mfcc17_mean', 'mfcc14_mean', 'perceptr_mean', 'mfcc5_var']
-- Actually uncorrelated features: [confirmed via MIC]
[]
-- 1 Correlated features: [Pearson]
['spectral_centroid_mean']
Model: "MLP"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_188 (Dense) (None, 128) 7296
dropout_146 (Dropout) (None, 128) 0
dense_189 (Dense) (None, 64) 8256
dropout_147 (Dropout) (None, 64) 0
dense_190 (Dense) (None, 64) 4160
dropout_148 (Dropout) (None, 64) 0
dense_191 (Dense) (None, 10) 650
=================================================================
Total params: 20,362
Trainable params: 20,362
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
7/7 [==============================] - 2s 84ms/step - loss: 3.0539 - accuracy: 0.0901 - recall: 0.0175 - precision: 0.1474 - AUROC: 0.5078 - AUPRC: 0.1010 - f1_score: 0.0313 - balanced_accuracy: 0.5031 - specificity: 0.9887 - miss_rate: 0.9825 - fall_out: 0.0113 - mcc: 0.0173 - val_loss: 2.3802 - val_accuracy: 0.1150 - val_recall: 0.0150 - val_precision: 0.5000 - val_AUROC: 0.5867 - val_AUPRC: 0.1301 - val_f1_score: 0.0291 - val_balanced_accuracy: 0.5067 - val_specificity: 0.9983 - val_miss_rate: 0.9850 - val_fall_out: 0.0017 - val_mcc: 0.0731
Epoch 2/100
7/7 [==============================] - 0s 15ms/step - loss: 2.6658 - accuracy: 0.1302 - recall: 0.0150 - precision: 0.2034 - AUROC: 0.5453 - AUPRC: 0.1178 - f1_score: 0.0280 - balanced_accuracy: 0.5042 - specificity: 0.9935 - miss_rate: 0.9850 - fall_out: 0.0065 - mcc: 0.0297 - val_loss: 2.2893 - val_accuracy: 0.2300 - val_recall: 0.0100 - val_precision: 0.6667 - val_AUROC: 0.6393 - val_AUPRC: 0.1631 - val_f1_score: 0.0197 - val_balanced_accuracy: 0.5047 - val_specificity: 0.9994 - val_miss_rate: 0.9900 - val_fall_out: 5.5556e-04 - val_mcc: 0.0732
Epoch 3/100
7/7 [==============================] - 0s 15ms/step - loss: 2.6340 - accuracy: 0.1527 - recall: 0.0175 - precision: 0.2414 - AUROC: 0.5730 - AUPRC: 0.1323 - f1_score: 0.0327 - balanced_accuracy: 0.5057 - specificity: 0.9939 - miss_rate: 0.9825 - fall_out: 0.0061 - mcc: 0.0403 - val_loss: 2.2257 - val_accuracy: 0.2950 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6843 - val_AUPRC: 0.2041 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.4997 - val_specificity: 0.9994 - val_miss_rate: 1.0000 - val_fall_out: 5.5556e-04 - val_mcc: -0.0075
Epoch 4/100
7/7 [==============================] - 0s 14ms/step - loss: 2.4182 - accuracy: 0.1690 - recall: 0.0088 - precision: 0.1591 - AUROC: 0.6023 - AUPRC: 0.1415 - f1_score: 0.0166 - balanced_accuracy: 0.5018 - specificity: 0.9949 - miss_rate: 0.9912 - fall_out: 0.0051 - mcc: 0.0147 - val_loss: 2.2079 - val_accuracy: 0.3200 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7232 - val_AUPRC: 0.2314 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.4997 - val_specificity: 0.9994 - val_miss_rate: 1.0000 - val_fall_out: 5.5556e-04 - val_mcc: -0.0075
Epoch 5/100
7/7 [==============================] - 0s 14ms/step - loss: 2.3787 - accuracy: 0.1477 - recall: 0.0113 - precision: 0.1731 - AUROC: 0.5975 - AUPRC: 0.1357 - f1_score: 0.0212 - balanced_accuracy: 0.5026 - specificity: 0.9940 - miss_rate: 0.9887 - fall_out: 0.0060 - mcc: 0.0197 - val_loss: 2.1883 - val_accuracy: 0.3250 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7400 - val_AUPRC: 0.2462 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.4994 - val_specificity: 0.9989 - val_miss_rate: 1.0000 - val_fall_out: 0.0011 - val_mcc: -0.0105
Epoch 6/100
7/7 [==============================] - 0s 13ms/step - loss: 2.3701 - accuracy: 0.1577 - recall: 0.0150 - precision: 0.3636 - AUROC: 0.6096 - AUPRC: 0.1510 - f1_score: 0.0288 - balanced_accuracy: 0.5060 - specificity: 0.9971 - miss_rate: 0.9850 - fall_out: 0.0029 - mcc: 0.0566 - val_loss: 2.1584 - val_accuracy: 0.3300 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7589 - val_AUPRC: 0.2644 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.4997 - val_specificity: 0.9994 - val_miss_rate: 1.0000 - val_fall_out: 5.5556e-04 - val_mcc: -0.0075
Epoch 7/100
7/7 [==============================] - 0s 14ms/step - loss: 2.3263 - accuracy: 0.2040 - recall: 0.0100 - precision: 0.2424 - AUROC: 0.6377 - AUPRC: 0.1716 - f1_score: 0.0192 - balanced_accuracy: 0.5033 - specificity: 0.9965 - miss_rate: 0.9900 - fall_out: 0.0035 - mcc: 0.0306 - val_loss: 2.1244 - val_accuracy: 0.3700 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7776 - val_AUPRC: 0.2794 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 8/100
7/7 [==============================] - 0s 12ms/step - loss: 2.3124 - accuracy: 0.2003 - recall: 0.0163 - precision: 0.2708 - AUROC: 0.6534 - AUPRC: 0.1752 - f1_score: 0.0307 - balanced_accuracy: 0.5057 - specificity: 0.9951 - miss_rate: 0.9837 - fall_out: 0.0049 - mcc: 0.0443 - val_loss: 2.0803 - val_accuracy: 0.3700 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7905 - val_AUPRC: 0.3003 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 9/100
7/7 [==============================] - 0s 11ms/step - loss: 2.2456 - accuracy: 0.2503 - recall: 0.0213 - precision: 0.4048 - AUROC: 0.6740 - AUPRC: 0.2051 - f1_score: 0.0404 - balanced_accuracy: 0.5089 - specificity: 0.9965 - miss_rate: 0.9787 - fall_out: 0.0035 - mcc: 0.0738 - val_loss: 2.0403 - val_accuracy: 0.3950 - val_recall: 0.0050 - val_precision: 1.0000 - val_AUROC: 0.8043 - val_AUPRC: 0.3317 - val_f1_score: 0.0100 - val_balanced_accuracy: 0.5025 - val_specificity: 1.0000 - val_miss_rate: 0.9950 - val_fall_out: 0.0000e+00 - val_mcc: 0.0671
Epoch 10/100
7/7 [==============================] - 0s 12ms/step - loss: 2.1559 - accuracy: 0.2678 - recall: 0.0263 - precision: 0.4286 - AUROC: 0.7116 - AUPRC: 0.2331 - f1_score: 0.0495 - balanced_accuracy: 0.5112 - specificity: 0.9961 - miss_rate: 0.9737 - fall_out: 0.0039 - mcc: 0.0860 - val_loss: 2.0018 - val_accuracy: 0.3550 - val_recall: 0.0250 - val_precision: 0.8333 - val_AUROC: 0.8116 - val_AUPRC: 0.3494 - val_f1_score: 0.0485 - val_balanced_accuracy: 0.5122 - val_specificity: 0.9994 - val_miss_rate: 0.9750 - val_fall_out: 5.5556e-04 - val_mcc: 0.1341
Epoch 11/100
7/7 [==============================] - 0s 12ms/step - loss: 2.0853 - accuracy: 0.2979 - recall: 0.0476 - precision: 0.5588 - AUROC: 0.7168 - AUPRC: 0.2641 - f1_score: 0.0877 - balanced_accuracy: 0.5217 - specificity: 0.9958 - miss_rate: 0.9524 - fall_out: 0.0042 - mcc: 0.1417 - val_loss: 1.9645 - val_accuracy: 0.3700 - val_recall: 0.0300 - val_precision: 0.8571 - val_AUROC: 0.8216 - val_AUPRC: 0.3684 - val_f1_score: 0.0580 - val_balanced_accuracy: 0.5147 - val_specificity: 0.9994 - val_miss_rate: 0.9700 - val_fall_out: 5.5556e-04 - val_mcc: 0.1496
Epoch 12/100
7/7 [==============================] - 0s 12ms/step - loss: 2.1532 - accuracy: 0.2716 - recall: 0.0426 - precision: 0.5075 - AUROC: 0.7191 - AUPRC: 0.2478 - f1_score: 0.0785 - balanced_accuracy: 0.5190 - specificity: 0.9954 - miss_rate: 0.9574 - fall_out: 0.0046 - mcc: 0.1249 - val_loss: 1.9359 - val_accuracy: 0.3600 - val_recall: 0.0350 - val_precision: 0.7778 - val_AUROC: 0.8276 - val_AUPRC: 0.3797 - val_f1_score: 0.0670 - val_balanced_accuracy: 0.5169 - val_specificity: 0.9989 - val_miss_rate: 0.9650 - val_fall_out: 0.0011 - val_mcc: 0.1519
Epoch 13/100
7/7 [==============================] - 0s 12ms/step - loss: 2.0026 - accuracy: 0.3016 - recall: 0.0476 - precision: 0.5846 - AUROC: 0.7546 - AUPRC: 0.2935 - f1_score: 0.0880 - balanced_accuracy: 0.5219 - specificity: 0.9962 - miss_rate: 0.9524 - fall_out: 0.0038 - mcc: 0.1463 - val_loss: 1.9014 - val_accuracy: 0.3900 - val_recall: 0.0450 - val_precision: 0.8182 - val_AUROC: 0.8323 - val_AUPRC: 0.3974 - val_f1_score: 0.0853 - val_balanced_accuracy: 0.5219 - val_specificity: 0.9989 - val_miss_rate: 0.9550 - val_fall_out: 0.0011 - val_mcc: 0.1780
Epoch 14/100
7/7 [==============================] - 0s 12ms/step - loss: 1.9904 - accuracy: 0.3154 - recall: 0.0651 - precision: 0.5474 - AUROC: 0.7542 - AUPRC: 0.2938 - f1_score: 0.1163 - balanced_accuracy: 0.5296 - specificity: 0.9940 - miss_rate: 0.9349 - fall_out: 0.0060 - mcc: 0.1636 - val_loss: 1.8777 - val_accuracy: 0.3800 - val_recall: 0.0500 - val_precision: 0.7692 - val_AUROC: 0.8382 - val_AUPRC: 0.3953 - val_f1_score: 0.0939 - val_balanced_accuracy: 0.5242 - val_specificity: 0.9983 - val_miss_rate: 0.9500 - val_fall_out: 0.0017 - val_mcc: 0.1804
Epoch 15/100
7/7 [==============================] - 0s 12ms/step - loss: 1.9253 - accuracy: 0.3579 - recall: 0.0751 - precision: 0.6061 - AUROC: 0.7714 - AUPRC: 0.3220 - f1_score: 0.1336 - balanced_accuracy: 0.5348 - specificity: 0.9946 - miss_rate: 0.9249 - fall_out: 0.0054 - mcc: 0.1889 - val_loss: 1.8316 - val_accuracy: 0.4000 - val_recall: 0.0700 - val_precision: 0.7368 - val_AUROC: 0.8443 - val_AUPRC: 0.4116 - val_f1_score: 0.1279 - val_balanced_accuracy: 0.5336 - val_specificity: 0.9972 - val_miss_rate: 0.9300 - val_fall_out: 0.0028 - val_mcc: 0.2079
Epoch 16/100
7/7 [==============================] - 0s 12ms/step - loss: 1.9610 - accuracy: 0.3492 - recall: 0.0864 - precision: 0.6699 - AUROC: 0.7797 - AUPRC: 0.3310 - f1_score: 0.1530 - balanced_accuracy: 0.5408 - specificity: 0.9953 - miss_rate: 0.9136 - fall_out: 0.0047 - mcc: 0.2171 - val_loss: 1.7892 - val_accuracy: 0.4100 - val_recall: 0.0900 - val_precision: 0.8182 - val_AUROC: 0.8512 - val_AUPRC: 0.4356 - val_f1_score: 0.1622 - val_balanced_accuracy: 0.5439 - val_specificity: 0.9978 - val_miss_rate: 0.9100 - val_fall_out: 0.0022 - val_mcc: 0.2525
Epoch 17/100
7/7 [==============================] - 0s 12ms/step - loss: 1.9634 - accuracy: 0.3379 - recall: 0.0914 - precision: 0.6033 - AUROC: 0.7766 - AUPRC: 0.3298 - f1_score: 0.1587 - balanced_accuracy: 0.5423 - specificity: 0.9933 - miss_rate: 0.9086 - fall_out: 0.0067 - mcc: 0.2080 - val_loss: 1.7536 - val_accuracy: 0.4200 - val_recall: 0.1100 - val_precision: 0.7857 - val_AUROC: 0.8586 - val_AUPRC: 0.4504 - val_f1_score: 0.1930 - val_balanced_accuracy: 0.5533 - val_specificity: 0.9967 - val_miss_rate: 0.8900 - val_fall_out: 0.0033 - val_mcc: 0.2724
Epoch 18/100
7/7 [==============================] - 0s 11ms/step - loss: 1.9360 - accuracy: 0.3429 - recall: 0.1076 - precision: 0.6143 - AUROC: 0.7838 - AUPRC: 0.3380 - f1_score: 0.1832 - balanced_accuracy: 0.5501 - specificity: 0.9925 - miss_rate: 0.8924 - fall_out: 0.0075 - mcc: 0.2289 - val_loss: 1.7210 - val_accuracy: 0.4500 - val_recall: 0.1300 - val_precision: 0.8125 - val_AUROC: 0.8644 - val_AUPRC: 0.4657 - val_f1_score: 0.2241 - val_balanced_accuracy: 0.5633 - val_specificity: 0.9967 - val_miss_rate: 0.8700 - val_fall_out: 0.0033 - val_mcc: 0.3028
Epoch 19/100
7/7 [==============================] - 0s 12ms/step - loss: 1.8259 - accuracy: 0.3630 - recall: 0.1289 - precision: 0.6603 - AUROC: 0.7952 - AUPRC: 0.3629 - f1_score: 0.2157 - balanced_accuracy: 0.5608 - specificity: 0.9926 - miss_rate: 0.8711 - fall_out: 0.0074 - mcc: 0.2635 - val_loss: 1.6979 - val_accuracy: 0.4600 - val_recall: 0.1250 - val_precision: 0.7812 - val_AUROC: 0.8700 - val_AUPRC: 0.4768 - val_f1_score: 0.2155 - val_balanced_accuracy: 0.5606 - val_specificity: 0.9961 - val_miss_rate: 0.8750 - val_fall_out: 0.0039 - val_mcc: 0.2896
Epoch 20/100
7/7 [==============================] - 0s 12ms/step - loss: 1.8261 - accuracy: 0.3579 - recall: 0.1339 - precision: 0.6446 - AUROC: 0.8033 - AUPRC: 0.3738 - f1_score: 0.2218 - balanced_accuracy: 0.5629 - specificity: 0.9918 - miss_rate: 0.8661 - fall_out: 0.0082 - mcc: 0.2644 - val_loss: 1.6707 - val_accuracy: 0.4650 - val_recall: 0.1250 - val_precision: 0.6944 - val_AUROC: 0.8704 - val_AUPRC: 0.4800 - val_f1_score: 0.2119 - val_balanced_accuracy: 0.5594 - val_specificity: 0.9939 - val_miss_rate: 0.8750 - val_fall_out: 0.0061 - val_mcc: 0.2683
Epoch 21/100
7/7 [==============================] - 0s 11ms/step - loss: 1.7998 - accuracy: 0.3805 - recall: 0.1539 - precision: 0.6910 - AUROC: 0.8102 - AUPRC: 0.3869 - f1_score: 0.2518 - balanced_accuracy: 0.5731 - specificity: 0.9924 - miss_rate: 0.8461 - fall_out: 0.0076 - mcc: 0.2974 - val_loss: 1.6510 - val_accuracy: 0.4650 - val_recall: 0.1250 - val_precision: 0.7143 - val_AUROC: 0.8731 - val_AUPRC: 0.4829 - val_f1_score: 0.2128 - val_balanced_accuracy: 0.5597 - val_specificity: 0.9944 - val_miss_rate: 0.8750 - val_fall_out: 0.0056 - val_mcc: 0.2733
Epoch 22/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7969 - accuracy: 0.3617 - recall: 0.1377 - precision: 0.6044 - AUROC: 0.8119 - AUPRC: 0.3803 - f1_score: 0.2243 - balanced_accuracy: 0.5638 - specificity: 0.9900 - miss_rate: 0.8623 - fall_out: 0.0100 - mcc: 0.2567 - val_loss: 1.6164 - val_accuracy: 0.4750 - val_recall: 0.1250 - val_precision: 0.7353 - val_AUROC: 0.8799 - val_AUPRC: 0.5016 - val_f1_score: 0.2137 - val_balanced_accuracy: 0.5600 - val_specificity: 0.9950 - val_miss_rate: 0.8750 - val_fall_out: 0.0050 - val_mcc: 0.2785
Epoch 23/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7158 - accuracy: 0.4043 - recall: 0.1364 - precision: 0.6301 - AUROC: 0.8338 - AUPRC: 0.4084 - f1_score: 0.2243 - balanced_accuracy: 0.5638 - specificity: 0.9911 - miss_rate: 0.8636 - fall_out: 0.0089 - mcc: 0.2628 - val_loss: 1.5878 - val_accuracy: 0.4900 - val_recall: 0.1400 - val_precision: 0.7568 - val_AUROC: 0.8822 - val_AUPRC: 0.5114 - val_f1_score: 0.2363 - val_balanced_accuracy: 0.5675 - val_specificity: 0.9950 - val_miss_rate: 0.8600 - val_fall_out: 0.0050 - val_mcc: 0.3006
Epoch 24/100
7/7 [==============================] - 0s 13ms/step - loss: 1.7050 - accuracy: 0.3855 - recall: 0.1552 - precision: 0.6631 - AUROC: 0.8312 - AUPRC: 0.4175 - f1_score: 0.2515 - balanced_accuracy: 0.5732 - specificity: 0.9912 - miss_rate: 0.8448 - fall_out: 0.0088 - mcc: 0.2906 - val_loss: 1.5651 - val_accuracy: 0.4900 - val_recall: 0.1500 - val_precision: 0.7143 - val_AUROC: 0.8840 - val_AUPRC: 0.5196 - val_f1_score: 0.2479 - val_balanced_accuracy: 0.5717 - val_specificity: 0.9933 - val_miss_rate: 0.8500 - val_fall_out: 0.0067 - val_mcc: 0.2999
Epoch 25/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7215 - accuracy: 0.3930 - recall: 0.1602 - precision: 0.6564 - AUROC: 0.8377 - AUPRC: 0.4142 - f1_score: 0.2575 - balanced_accuracy: 0.5754 - specificity: 0.9907 - miss_rate: 0.8398 - fall_out: 0.0093 - mcc: 0.2933 - val_loss: 1.5500 - val_accuracy: 0.4700 - val_recall: 0.1650 - val_precision: 0.6875 - val_AUROC: 0.8846 - val_AUPRC: 0.5225 - val_f1_score: 0.2661 - val_balanced_accuracy: 0.5783 - val_specificity: 0.9917 - val_miss_rate: 0.8350 - val_fall_out: 0.0083 - val_mcc: 0.3071
Epoch 26/100
7/7 [==============================] - 0s 11ms/step - loss: 1.7232 - accuracy: 0.4080 - recall: 0.1827 - precision: 0.6348 - AUROC: 0.8441 - AUPRC: 0.4250 - f1_score: 0.2838 - balanced_accuracy: 0.5855 - specificity: 0.9883 - miss_rate: 0.8173 - fall_out: 0.0117 - mcc: 0.3069 - val_loss: 1.5322 - val_accuracy: 0.5000 - val_recall: 0.1700 - val_precision: 0.7234 - val_AUROC: 0.8864 - val_AUPRC: 0.5306 - val_f1_score: 0.2753 - val_balanced_accuracy: 0.5814 - val_specificity: 0.9928 - val_miss_rate: 0.8300 - val_fall_out: 0.0072 - val_mcc: 0.3224
Epoch 27/100
7/7 [==============================] - 0s 12ms/step - loss: 1.6185 - accuracy: 0.4368 - recall: 0.1890 - precision: 0.7260 - AUROC: 0.8544 - AUPRC: 0.4741 - f1_score: 0.2999 - balanced_accuracy: 0.5905 - specificity: 0.9921 - miss_rate: 0.8110 - fall_out: 0.0079 - mcc: 0.3411 - val_loss: 1.5147 - val_accuracy: 0.5000 - val_recall: 0.1850 - val_precision: 0.6852 - val_AUROC: 0.8866 - val_AUPRC: 0.5310 - val_f1_score: 0.2913 - val_balanced_accuracy: 0.5878 - val_specificity: 0.9906 - val_miss_rate: 0.8150 - val_fall_out: 0.0094 - val_mcc: 0.3249
Epoch 28/100
7/7 [==============================] - 0s 13ms/step - loss: 1.6459 - accuracy: 0.4406 - recall: 0.1877 - precision: 0.6667 - AUROC: 0.8450 - AUPRC: 0.4540 - f1_score: 0.2930 - balanced_accuracy: 0.5887 - specificity: 0.9896 - miss_rate: 0.8123 - fall_out: 0.0104 - mcc: 0.3215 - val_loss: 1.4960 - val_accuracy: 0.5150 - val_recall: 0.1900 - val_precision: 0.7037 - val_AUROC: 0.8888 - val_AUPRC: 0.5379 - val_f1_score: 0.2992 - val_balanced_accuracy: 0.5906 - val_specificity: 0.9911 - val_miss_rate: 0.8100 - val_fall_out: 0.0089 - val_mcc: 0.3352
Epoch 29/100
7/7 [==============================] - 0s 13ms/step - loss: 1.5881 - accuracy: 0.4343 - recall: 0.1965 - precision: 0.6709 - AUROC: 0.8549 - AUPRC: 0.4658 - f1_score: 0.3040 - balanced_accuracy: 0.5929 - specificity: 0.9893 - miss_rate: 0.8035 - fall_out: 0.0107 - mcc: 0.3306 - val_loss: 1.4865 - val_accuracy: 0.5150 - val_recall: 0.1950 - val_precision: 0.6724 - val_AUROC: 0.8898 - val_AUPRC: 0.5376 - val_f1_score: 0.3023 - val_balanced_accuracy: 0.5922 - val_specificity: 0.9894 - val_miss_rate: 0.8050 - val_fall_out: 0.0106 - val_mcc: 0.3297
Epoch 30/100
7/7 [==============================] - 0s 13ms/step - loss: 1.5431 - accuracy: 0.4718 - recall: 0.2353 - precision: 0.7642 - AUROC: 0.8608 - AUPRC: 0.5024 - f1_score: 0.3598 - balanced_accuracy: 0.6136 - specificity: 0.9919 - miss_rate: 0.7647 - fall_out: 0.0081 - mcc: 0.3946 - val_loss: 1.4724 - val_accuracy: 0.5050 - val_recall: 0.2250 - val_precision: 0.6923 - val_AUROC: 0.8906 - val_AUPRC: 0.5418 - val_f1_score: 0.3396 - val_balanced_accuracy: 0.6069 - val_specificity: 0.9889 - val_miss_rate: 0.7750 - val_fall_out: 0.0111 - val_mcc: 0.3619
Epoch 31/100
7/7 [==============================] - 0s 13ms/step - loss: 1.5859 - accuracy: 0.4593 - recall: 0.2203 - precision: 0.6929 - AUROC: 0.8597 - AUPRC: 0.4678 - f1_score: 0.3343 - balanced_accuracy: 0.6047 - specificity: 0.9892 - miss_rate: 0.7797 - fall_out: 0.0108 - mcc: 0.3581 - val_loss: 1.4580 - val_accuracy: 0.5100 - val_recall: 0.2150 - val_precision: 0.6615 - val_AUROC: 0.8938 - val_AUPRC: 0.5484 - val_f1_score: 0.3245 - val_balanced_accuracy: 0.6014 - val_specificity: 0.9878 - val_miss_rate: 0.7850 - val_fall_out: 0.0122 - val_mcc: 0.3431
Epoch 32/100
7/7 [==============================] - 0s 12ms/step - loss: 1.5770 - accuracy: 0.4543 - recall: 0.2140 - precision: 0.6287 - AUROC: 0.8610 - AUPRC: 0.4625 - f1_score: 0.3193 - balanced_accuracy: 0.6000 - specificity: 0.9860 - miss_rate: 0.7860 - fall_out: 0.0140 - mcc: 0.3308 - val_loss: 1.4391 - val_accuracy: 0.5100 - val_recall: 0.2250 - val_precision: 0.6923 - val_AUROC: 0.8964 - val_AUPRC: 0.5574 - val_f1_score: 0.3396 - val_balanced_accuracy: 0.6069 - val_specificity: 0.9889 - val_miss_rate: 0.7750 - val_fall_out: 0.0111 - val_mcc: 0.3619
Epoch 33/100
7/7 [==============================] - 0s 12ms/step - loss: 1.5329 - accuracy: 0.4781 - recall: 0.2215 - precision: 0.7080 - AUROC: 0.8697 - AUPRC: 0.4980 - f1_score: 0.3375 - balanced_accuracy: 0.6057 - specificity: 0.9898 - miss_rate: 0.7785 - fall_out: 0.0102 - mcc: 0.3642 - val_loss: 1.4266 - val_accuracy: 0.5050 - val_recall: 0.2450 - val_precision: 0.7000 - val_AUROC: 0.8960 - val_AUPRC: 0.5650 - val_f1_score: 0.3630 - val_balanced_accuracy: 0.6167 - val_specificity: 0.9883 - val_miss_rate: 0.7550 - val_fall_out: 0.0117 - val_mcc: 0.3809
Epoch 34/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4870 - accuracy: 0.4743 - recall: 0.2340 - precision: 0.7004 - AUROC: 0.8766 - AUPRC: 0.5059 - f1_score: 0.3508 - balanced_accuracy: 0.6115 - specificity: 0.9889 - miss_rate: 0.7660 - fall_out: 0.0111 - mcc: 0.3721 - val_loss: 1.4123 - val_accuracy: 0.5200 - val_recall: 0.2650 - val_precision: 0.7067 - val_AUROC: 0.8965 - val_AUPRC: 0.5717 - val_f1_score: 0.3855 - val_balanced_accuracy: 0.6264 - val_specificity: 0.9878 - val_miss_rate: 0.7350 - val_fall_out: 0.0122 - val_mcc: 0.3992
Epoch 35/100
7/7 [==============================] - 0s 12ms/step - loss: 1.5374 - accuracy: 0.4831 - recall: 0.2503 - precision: 0.7018 - AUROC: 0.8676 - AUPRC: 0.4912 - f1_score: 0.3690 - balanced_accuracy: 0.6192 - specificity: 0.9882 - miss_rate: 0.7497 - fall_out: 0.0118 - mcc: 0.3858 - val_loss: 1.4021 - val_accuracy: 0.5250 - val_recall: 0.2550 - val_precision: 0.6800 - val_AUROC: 0.8999 - val_AUPRC: 0.5760 - val_f1_score: 0.3709 - val_balanced_accuracy: 0.6208 - val_specificity: 0.9867 - val_miss_rate: 0.7450 - val_fall_out: 0.0133 - val_mcc: 0.3816
Epoch 36/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4690 - accuracy: 0.4931 - recall: 0.2666 - precision: 0.7076 - AUROC: 0.8807 - AUPRC: 0.5176 - f1_score: 0.3873 - balanced_accuracy: 0.6272 - specificity: 0.9878 - miss_rate: 0.7334 - fall_out: 0.0122 - mcc: 0.4008 - val_loss: 1.3887 - val_accuracy: 0.5350 - val_recall: 0.2650 - val_precision: 0.6974 - val_AUROC: 0.9013 - val_AUPRC: 0.5786 - val_f1_score: 0.3841 - val_balanced_accuracy: 0.6261 - val_specificity: 0.9872 - val_miss_rate: 0.7350 - val_fall_out: 0.0128 - val_mcc: 0.3958
Epoch 37/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4119 - accuracy: 0.5044 - recall: 0.2753 - precision: 0.7483 - AUROC: 0.8908 - AUPRC: 0.5505 - f1_score: 0.4026 - balanced_accuracy: 0.6325 - specificity: 0.9897 - miss_rate: 0.7247 - fall_out: 0.0103 - mcc: 0.4224 - val_loss: 1.3812 - val_accuracy: 0.5350 - val_recall: 0.2550 - val_precision: 0.6892 - val_AUROC: 0.9011 - val_AUPRC: 0.5751 - val_f1_score: 0.3723 - val_balanced_accuracy: 0.6211 - val_specificity: 0.9872 - val_miss_rate: 0.7450 - val_fall_out: 0.0128 - val_mcc: 0.3850
Epoch 38/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4245 - accuracy: 0.5081 - recall: 0.2816 - precision: 0.7235 - AUROC: 0.8873 - AUPRC: 0.5420 - f1_score: 0.4054 - balanced_accuracy: 0.6348 - specificity: 0.9880 - miss_rate: 0.7184 - fall_out: 0.0120 - mcc: 0.4182 - val_loss: 1.3651 - val_accuracy: 0.5700 - val_recall: 0.2700 - val_precision: 0.7013 - val_AUROC: 0.9023 - val_AUPRC: 0.5800 - val_f1_score: 0.3899 - val_balanced_accuracy: 0.6286 - val_specificity: 0.9872 - val_miss_rate: 0.7300 - val_fall_out: 0.0128 - val_mcc: 0.4011
Epoch 39/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4673 - accuracy: 0.4944 - recall: 0.2778 - precision: 0.7161 - AUROC: 0.8809 - AUPRC: 0.5249 - f1_score: 0.4004 - balanced_accuracy: 0.6328 - specificity: 0.9878 - miss_rate: 0.7222 - fall_out: 0.0122 - mcc: 0.4126 - val_loss: 1.3534 - val_accuracy: 0.5750 - val_recall: 0.2750 - val_precision: 0.7051 - val_AUROC: 0.9037 - val_AUPRC: 0.5899 - val_f1_score: 0.3957 - val_balanced_accuracy: 0.6311 - val_specificity: 0.9872 - val_miss_rate: 0.7250 - val_fall_out: 0.0128 - val_mcc: 0.4063
Epoch 40/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4357 - accuracy: 0.4881 - recall: 0.2578 - precision: 0.7203 - AUROC: 0.8860 - AUPRC: 0.5365 - f1_score: 0.3797 - balanced_accuracy: 0.6233 - specificity: 0.9889 - miss_rate: 0.7422 - fall_out: 0.0111 - mcc: 0.3984 - val_loss: 1.3381 - val_accuracy: 0.5550 - val_recall: 0.2750 - val_precision: 0.7143 - val_AUROC: 0.9052 - val_AUPRC: 0.5962 - val_f1_score: 0.3971 - val_balanced_accuracy: 0.6314 - val_specificity: 0.9878 - val_miss_rate: 0.7250 - val_fall_out: 0.0122 - val_mcc: 0.4097
Epoch 41/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3784 - accuracy: 0.5169 - recall: 0.3029 - precision: 0.7333 - AUROC: 0.8964 - AUPRC: 0.5796 - f1_score: 0.4287 - balanced_accuracy: 0.6453 - specificity: 0.9878 - miss_rate: 0.6971 - fall_out: 0.0122 - mcc: 0.4382 - val_loss: 1.3248 - val_accuracy: 0.5750 - val_recall: 0.2800 - val_precision: 0.7273 - val_AUROC: 0.9062 - val_AUPRC: 0.6036 - val_f1_score: 0.4043 - val_balanced_accuracy: 0.6342 - val_specificity: 0.9883 - val_miss_rate: 0.7200 - val_fall_out: 0.0117 - val_mcc: 0.4184
Epoch 42/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3992 - accuracy: 0.5181 - recall: 0.2916 - precision: 0.7373 - AUROC: 0.8908 - AUPRC: 0.5583 - f1_score: 0.4179 - balanced_accuracy: 0.6400 - specificity: 0.9885 - miss_rate: 0.7084 - fall_out: 0.0115 - mcc: 0.4311 - val_loss: 1.3050 - val_accuracy: 0.5700 - val_recall: 0.2900 - val_precision: 0.7250 - val_AUROC: 0.9086 - val_AUPRC: 0.6126 - val_f1_score: 0.4143 - val_balanced_accuracy: 0.6389 - val_specificity: 0.9878 - val_miss_rate: 0.7100 - val_fall_out: 0.0122 - val_mcc: 0.4253
Epoch 43/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4003 - accuracy: 0.4931 - recall: 0.2879 - precision: 0.7055 - AUROC: 0.8906 - AUPRC: 0.5437 - f1_score: 0.4089 - balanced_accuracy: 0.6373 - specificity: 0.9866 - miss_rate: 0.7121 - fall_out: 0.0134 - mcc: 0.4163 - val_loss: 1.2957 - val_accuracy: 0.5800 - val_recall: 0.2950 - val_precision: 0.7375 - val_AUROC: 0.9108 - val_AUPRC: 0.6211 - val_f1_score: 0.4214 - val_balanced_accuracy: 0.6417 - val_specificity: 0.9883 - val_miss_rate: 0.7050 - val_fall_out: 0.0117 - val_mcc: 0.4338
Epoch 44/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3957 - accuracy: 0.5369 - recall: 0.3004 - precision: 0.7430 - AUROC: 0.8947 - AUPRC: 0.5710 - f1_score: 0.4278 - balanced_accuracy: 0.6444 - specificity: 0.9885 - miss_rate: 0.6996 - fall_out: 0.0115 - mcc: 0.4399 - val_loss: 1.2974 - val_accuracy: 0.5900 - val_recall: 0.2850 - val_precision: 0.7403 - val_AUROC: 0.9095 - val_AUPRC: 0.6150 - val_f1_score: 0.4116 - val_balanced_accuracy: 0.6369 - val_specificity: 0.9889 - val_miss_rate: 0.7150 - val_fall_out: 0.0111 - val_mcc: 0.4271
Epoch 45/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3420 - accuracy: 0.5106 - recall: 0.3104 - precision: 0.7447 - AUROC: 0.9013 - AUPRC: 0.5798 - f1_score: 0.4382 - balanced_accuracy: 0.6493 - specificity: 0.9882 - miss_rate: 0.6896 - fall_out: 0.0118 - mcc: 0.4482 - val_loss: 1.2846 - val_accuracy: 0.5850 - val_recall: 0.2850 - val_precision: 0.7308 - val_AUROC: 0.9109 - val_AUPRC: 0.6231 - val_f1_score: 0.4101 - val_balanced_accuracy: 0.6367 - val_specificity: 0.9883 - val_miss_rate: 0.7150 - val_fall_out: 0.0117 - val_mcc: 0.4236
Epoch 46/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3336 - accuracy: 0.5357 - recall: 0.3004 - precision: 0.7186 - AUROC: 0.9030 - AUPRC: 0.5714 - f1_score: 0.4237 - balanced_accuracy: 0.6437 - specificity: 0.9869 - miss_rate: 0.6996 - fall_out: 0.0131 - mcc: 0.4307 - val_loss: 1.2858 - val_accuracy: 0.5900 - val_recall: 0.2900 - val_precision: 0.7342 - val_AUROC: 0.9101 - val_AUPRC: 0.6172 - val_f1_score: 0.4158 - val_balanced_accuracy: 0.6392 - val_specificity: 0.9883 - val_miss_rate: 0.7100 - val_fall_out: 0.0117 - val_mcc: 0.4287
Epoch 47/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3508 - accuracy: 0.5244 - recall: 0.3104 - precision: 0.7381 - AUROC: 0.9023 - AUPRC: 0.5747 - f1_score: 0.4370 - balanced_accuracy: 0.6491 - specificity: 0.9878 - miss_rate: 0.6896 - fall_out: 0.0122 - mcc: 0.4456 - val_loss: 1.2770 - val_accuracy: 0.6100 - val_recall: 0.3050 - val_precision: 0.7262 - val_AUROC: 0.9109 - val_AUPRC: 0.6216 - val_f1_score: 0.4296 - val_balanced_accuracy: 0.6461 - val_specificity: 0.9872 - val_miss_rate: 0.6950 - val_fall_out: 0.0128 - val_mcc: 0.4370
Epoch 48/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3089 - accuracy: 0.5532 - recall: 0.3367 - precision: 0.7330 - AUROC: 0.9110 - AUPRC: 0.6017 - f1_score: 0.4614 - balanced_accuracy: 0.6615 - specificity: 0.9864 - miss_rate: 0.6633 - fall_out: 0.0136 - mcc: 0.4629 - val_loss: 1.2620 - val_accuracy: 0.5950 - val_recall: 0.3300 - val_precision: 0.7333 - val_AUROC: 0.9112 - val_AUPRC: 0.6312 - val_f1_score: 0.4552 - val_balanced_accuracy: 0.6583 - val_specificity: 0.9867 - val_miss_rate: 0.6700 - val_fall_out: 0.0133 - val_mcc: 0.4583
Epoch 49/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2966 - accuracy: 0.5519 - recall: 0.3529 - precision: 0.7581 - AUROC: 0.9105 - AUPRC: 0.6077 - f1_score: 0.4816 - balanced_accuracy: 0.6702 - specificity: 0.9875 - miss_rate: 0.6471 - fall_out: 0.0125 - mcc: 0.4847 - val_loss: 1.2458 - val_accuracy: 0.6100 - val_recall: 0.3750 - val_precision: 0.7732 - val_AUROC: 0.9130 - val_AUPRC: 0.6386 - val_f1_score: 0.5051 - val_balanced_accuracy: 0.6814 - val_specificity: 0.9878 - val_miss_rate: 0.6250 - val_fall_out: 0.0122 - val_mcc: 0.5066
Epoch 50/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2484 - accuracy: 0.5594 - recall: 0.3529 - precision: 0.7460 - AUROC: 0.9139 - AUPRC: 0.6200 - f1_score: 0.4792 - balanced_accuracy: 0.6698 - specificity: 0.9866 - miss_rate: 0.6471 - fall_out: 0.0134 - mcc: 0.4799 - val_loss: 1.2290 - val_accuracy: 0.5950 - val_recall: 0.3600 - val_precision: 0.7579 - val_AUROC: 0.9149 - val_AUPRC: 0.6415 - val_f1_score: 0.4881 - val_balanced_accuracy: 0.6736 - val_specificity: 0.9872 - val_miss_rate: 0.6400 - val_fall_out: 0.0128 - val_mcc: 0.4897
Epoch 51/100
7/7 [==============================] - 0s 13ms/step - loss: 1.2949 - accuracy: 0.5670 - recall: 0.3517 - precision: 0.7574 - AUROC: 0.9083 - AUPRC: 0.6012 - f1_score: 0.4803 - balanced_accuracy: 0.6696 - specificity: 0.9875 - miss_rate: 0.6483 - fall_out: 0.0125 - mcc: 0.4836 - val_loss: 1.2201 - val_accuracy: 0.6050 - val_recall: 0.3750 - val_precision: 0.7653 - val_AUROC: 0.9173 - val_AUPRC: 0.6447 - val_f1_score: 0.5034 - val_balanced_accuracy: 0.6811 - val_specificity: 0.9872 - val_miss_rate: 0.6250 - val_fall_out: 0.0128 - val_mcc: 0.5034
Epoch 52/100
7/7 [==============================] - 0s 14ms/step - loss: 1.2711 - accuracy: 0.5695 - recall: 0.3692 - precision: 0.7846 - AUROC: 0.9156 - AUPRC: 0.6402 - f1_score: 0.5021 - balanced_accuracy: 0.6790 - specificity: 0.9887 - miss_rate: 0.6308 - fall_out: 0.0113 - mcc: 0.5071 - val_loss: 1.1992 - val_accuracy: 0.6000 - val_recall: 0.3750 - val_precision: 0.7812 - val_AUROC: 0.9194 - val_AUPRC: 0.6551 - val_f1_score: 0.5068 - val_balanced_accuracy: 0.6817 - val_specificity: 0.9883 - val_miss_rate: 0.6250 - val_fall_out: 0.0117 - val_mcc: 0.5099
Epoch 53/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2258 - accuracy: 0.5594 - recall: 0.3792 - precision: 0.7500 - AUROC: 0.9166 - AUPRC: 0.6317 - f1_score: 0.5037 - balanced_accuracy: 0.6826 - specificity: 0.9860 - miss_rate: 0.6208 - fall_out: 0.0140 - mcc: 0.5000 - val_loss: 1.1927 - val_accuracy: 0.6000 - val_recall: 0.3700 - val_precision: 0.7789 - val_AUROC: 0.9205 - val_AUPRC: 0.6553 - val_f1_score: 0.5017 - val_balanced_accuracy: 0.6792 - val_specificity: 0.9883 - val_miss_rate: 0.6300 - val_fall_out: 0.0117 - val_mcc: 0.5054
Epoch 54/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2053 - accuracy: 0.5607 - recall: 0.3642 - precision: 0.7678 - AUROC: 0.9213 - AUPRC: 0.6374 - f1_score: 0.4941 - balanced_accuracy: 0.6760 - specificity: 0.9878 - miss_rate: 0.6358 - fall_out: 0.0122 - mcc: 0.4967 - val_loss: 1.1980 - val_accuracy: 0.6100 - val_recall: 0.3850 - val_precision: 0.7778 - val_AUROC: 0.9187 - val_AUPRC: 0.6560 - val_f1_score: 0.5151 - val_balanced_accuracy: 0.6864 - val_specificity: 0.9878 - val_miss_rate: 0.6150 - val_fall_out: 0.0122 - val_mcc: 0.5156
Epoch 55/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2041 - accuracy: 0.5770 - recall: 0.3742 - precision: 0.7686 - AUROC: 0.9206 - AUPRC: 0.6451 - f1_score: 0.5034 - balanced_accuracy: 0.6809 - specificity: 0.9875 - miss_rate: 0.6258 - fall_out: 0.0125 - mcc: 0.5042 - val_loss: 1.1997 - val_accuracy: 0.6300 - val_recall: 0.4000 - val_precision: 0.7767 - val_AUROC: 0.9173 - val_AUPRC: 0.6559 - val_f1_score: 0.5281 - val_balanced_accuracy: 0.6936 - val_specificity: 0.9872 - val_miss_rate: 0.6000 - val_fall_out: 0.0128 - val_mcc: 0.5256
25/25 [==============================] - 0s 4ms/step - loss: 0.8495 - accuracy: 0.7447 - recall: 0.4944 - precision: 0.9101 - AUROC: 0.9709 - AUPRC: 0.8290 - f1_score: 0.6407 - balanced_accuracy: 0.7445 - specificity: 0.9946 - miss_rate: 0.5056 - fall_out: 0.0054 - mcc: 0.6472
7/7 [==============================] - 0s 4ms/step - loss: 1.1997 - accuracy: 0.6300 - recall: 0.4000 - precision: 0.7767 - AUROC: 0.9173 - AUPRC: 0.6559 - f1_score: 0.5281 - balanced_accuracy: 0.6936 - specificity: 0.9872 - miss_rate: 0.6000 - fall_out: 0.0128 - mcc: 0.5256
3it [00:25, 8.19s/it]
-- HOLDOUT 4 -- WINDOW window_30s
-- 23 Uncorrelated features: [Pearson+Spearman]
['mfcc16_mean', 'harmony_mean', 'mfcc10_var', 'mfcc4_mean', 'mfcc15_mean', 'mfcc19_mean', 'mfcc6_mean', 'mfcc8_var', 'mfcc9_var', 'mfcc3_mean', 'mfcc17_var', 'chroma_stft_var', 'mfcc3_var', 'mfcc1_var', 'mfcc2_var', 'mfcc7_var', 'tempo', 'mfcc13_mean', 'mfcc5_mean', 'mfcc17_mean', 'mfcc14_mean', 'perceptr_mean', 'mfcc5_var']
-- Actually uncorrelated features: [confirmed via MIC]
[]
-- 1 Correlated features: [Pearson]
['spectral_centroid_mean']
Model: "MLP"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_192 (Dense) (None, 128) 7296
dropout_149 (Dropout) (None, 128) 0
dense_193 (Dense) (None, 64) 8256
dropout_150 (Dropout) (None, 64) 0
dense_194 (Dense) (None, 64) 4160
dropout_151 (Dropout) (None, 64) 0
dense_195 (Dense) (None, 10) 650
=================================================================
Total params: 20,362
Trainable params: 20,362
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
7/7 [==============================] - 2s 84ms/step - loss: 2.9913 - accuracy: 0.1101 - recall: 0.0138 - precision: 0.1111 - AUROC: 0.5127 - AUPRC: 0.1061 - f1_score: 0.0245 - balanced_accuracy: 0.5008 - specificity: 0.9878 - miss_rate: 0.9862 - fall_out: 0.0122 - mcc: 0.0041 - val_loss: 2.3242 - val_accuracy: 0.1500 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5328 - val_AUPRC: 0.1151 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.4986 - val_specificity: 0.9972 - val_miss_rate: 1.0000 - val_fall_out: 0.0028 - val_mcc: -0.0167
Epoch 2/100
7/7 [==============================] - 0s 16ms/step - loss: 2.6980 - accuracy: 0.1402 - recall: 0.0175 - precision: 0.2154 - AUROC: 0.5290 - AUPRC: 0.1171 - f1_score: 0.0324 - balanced_accuracy: 0.5052 - specificity: 0.9929 - miss_rate: 0.9825 - fall_out: 0.0071 - mcc: 0.0348 - val_loss: 2.2529 - val_accuracy: 0.1600 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5874 - val_AUPRC: 0.1444 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.4997 - val_specificity: 0.9994 - val_miss_rate: 1.0000 - val_fall_out: 5.5556e-04 - val_mcc: -0.0075
Epoch 3/100
7/7 [==============================] - 0s 16ms/step - loss: 2.6414 - accuracy: 0.1389 - recall: 0.0150 - precision: 0.2105 - AUROC: 0.5397 - AUPRC: 0.1197 - f1_score: 0.0280 - balanced_accuracy: 0.5044 - specificity: 0.9937 - miss_rate: 0.9850 - fall_out: 0.0063 - mcc: 0.0312 - val_loss: 2.2171 - val_accuracy: 0.1950 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6315 - val_AUPRC: 0.1785 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 4/100
7/7 [==============================] - 0s 14ms/step - loss: 2.4838 - accuracy: 0.1589 - recall: 0.0050 - precision: 0.1081 - AUROC: 0.5672 - AUPRC: 0.1298 - f1_score: 0.0096 - balanced_accuracy: 0.5002 - specificity: 0.9954 - miss_rate: 0.9950 - fall_out: 0.0046 - mcc: 0.0018 - val_loss: 2.1921 - val_accuracy: 0.2100 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6680 - val_AUPRC: 0.2043 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 5/100
7/7 [==============================] - 0s 14ms/step - loss: 2.4057 - accuracy: 0.1740 - recall: 0.0100 - precision: 0.2105 - AUROC: 0.5837 - AUPRC: 0.1415 - f1_score: 0.0191 - balanced_accuracy: 0.5029 - specificity: 0.9958 - miss_rate: 0.9900 - fall_out: 0.0042 - mcc: 0.0255 - val_loss: 2.1698 - val_accuracy: 0.2450 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6854 - val_AUPRC: 0.2329 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 6/100
7/7 [==============================] - 0s 14ms/step - loss: 2.3401 - accuracy: 0.2128 - recall: 0.0138 - precision: 0.2895 - AUROC: 0.6210 - AUPRC: 0.1646 - f1_score: 0.0263 - balanced_accuracy: 0.5050 - specificity: 0.9962 - miss_rate: 0.9862 - fall_out: 0.0038 - mcc: 0.0437 - val_loss: 2.1361 - val_accuracy: 0.2800 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7111 - val_AUPRC: 0.2702 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 7/100
7/7 [==============================] - 0s 13ms/step - loss: 2.3345 - accuracy: 0.2003 - recall: 0.0150 - precision: 0.3333 - AUROC: 0.6364 - AUPRC: 0.1750 - f1_score: 0.0287 - balanced_accuracy: 0.5058 - specificity: 0.9967 - miss_rate: 0.9850 - fall_out: 0.0033 - mcc: 0.0523 - val_loss: 2.1013 - val_accuracy: 0.3100 - val_recall: 0.0050 - val_precision: 1.0000 - val_AUROC: 0.7396 - val_AUPRC: 0.3027 - val_f1_score: 0.0100 - val_balanced_accuracy: 0.5025 - val_specificity: 1.0000 - val_miss_rate: 0.9950 - val_fall_out: 0.0000e+00 - val_mcc: 0.0671
Epoch 8/100
7/7 [==============================] - 0s 14ms/step - loss: 2.2191 - accuracy: 0.2253 - recall: 0.0213 - precision: 0.4146 - AUROC: 0.6608 - AUPRC: 0.1963 - f1_score: 0.0405 - balanced_accuracy: 0.5090 - specificity: 0.9967 - miss_rate: 0.9787 - fall_out: 0.0033 - mcc: 0.0753 - val_loss: 2.0545 - val_accuracy: 0.3200 - val_recall: 0.0050 - val_precision: 1.0000 - val_AUROC: 0.7606 - val_AUPRC: 0.3283 - val_f1_score: 0.0100 - val_balanced_accuracy: 0.5025 - val_specificity: 1.0000 - val_miss_rate: 0.9950 - val_fall_out: 0.0000e+00 - val_mcc: 0.0671
Epoch 9/100
7/7 [==============================] - 0s 13ms/step - loss: 2.1861 - accuracy: 0.2315 - recall: 0.0363 - precision: 0.5370 - AUROC: 0.6672 - AUPRC: 0.2070 - f1_score: 0.0680 - balanced_accuracy: 0.5164 - specificity: 0.9965 - miss_rate: 0.9637 - fall_out: 0.0035 - mcc: 0.1202 - val_loss: 2.0239 - val_accuracy: 0.3150 - val_recall: 0.0150 - val_precision: 1.0000 - val_AUROC: 0.7715 - val_AUPRC: 0.3370 - val_f1_score: 0.0296 - val_balanced_accuracy: 0.5075 - val_specificity: 1.0000 - val_miss_rate: 0.9850 - val_fall_out: 0.0000e+00 - val_mcc: 0.1163
Epoch 10/100
7/7 [==============================] - 0s 12ms/step - loss: 2.1332 - accuracy: 0.2340 - recall: 0.0363 - precision: 0.4754 - AUROC: 0.6802 - AUPRC: 0.2249 - f1_score: 0.0674 - balanced_accuracy: 0.5159 - specificity: 0.9955 - miss_rate: 0.9637 - fall_out: 0.0045 - mcc: 0.1098 - val_loss: 1.9981 - val_accuracy: 0.3250 - val_recall: 0.0250 - val_precision: 1.0000 - val_AUROC: 0.7818 - val_AUPRC: 0.3473 - val_f1_score: 0.0488 - val_balanced_accuracy: 0.5125 - val_specificity: 1.0000 - val_miss_rate: 0.9750 - val_fall_out: 0.0000e+00 - val_mcc: 0.1502
Epoch 11/100
7/7 [==============================] - 0s 13ms/step - loss: 2.1512 - accuracy: 0.2816 - recall: 0.0413 - precision: 0.5690 - AUROC: 0.7026 - AUPRC: 0.2468 - f1_score: 0.0770 - balanced_accuracy: 0.5189 - specificity: 0.9965 - miss_rate: 0.9587 - fall_out: 0.0035 - mcc: 0.1337 - val_loss: 1.9766 - val_accuracy: 0.3450 - val_recall: 0.0350 - val_precision: 1.0000 - val_AUROC: 0.7804 - val_AUPRC: 0.3569 - val_f1_score: 0.0676 - val_balanced_accuracy: 0.5175 - val_specificity: 1.0000 - val_miss_rate: 0.9650 - val_fall_out: 0.0000e+00 - val_mcc: 0.1778
Epoch 12/100
7/7 [==============================] - 0s 12ms/step - loss: 2.1168 - accuracy: 0.2854 - recall: 0.0563 - precision: 0.5696 - AUROC: 0.6970 - AUPRC: 0.2529 - f1_score: 0.1025 - balanced_accuracy: 0.5258 - specificity: 0.9953 - miss_rate: 0.9437 - fall_out: 0.0047 - mcc: 0.1564 - val_loss: 1.9414 - val_accuracy: 0.3450 - val_recall: 0.0550 - val_precision: 1.0000 - val_AUROC: 0.7908 - val_AUPRC: 0.3704 - val_f1_score: 0.1043 - val_balanced_accuracy: 0.5275 - val_specificity: 1.0000 - val_miss_rate: 0.9450 - val_fall_out: 0.0000e+00 - val_mcc: 0.2231
Epoch 13/100
7/7 [==============================] - 0s 12ms/step - loss: 2.1574 - accuracy: 0.2829 - recall: 0.0638 - precision: 0.6296 - AUROC: 0.7090 - AUPRC: 0.2629 - f1_score: 0.1159 - balanced_accuracy: 0.5298 - specificity: 0.9958 - miss_rate: 0.9362 - fall_out: 0.0042 - mcc: 0.1787 - val_loss: 1.9084 - val_accuracy: 0.3500 - val_recall: 0.0550 - val_precision: 0.9167 - val_AUROC: 0.8001 - val_AUPRC: 0.3840 - val_f1_score: 0.1038 - val_balanced_accuracy: 0.5272 - val_specificity: 0.9994 - val_miss_rate: 0.9450 - val_fall_out: 5.5556e-04 - val_mcc: 0.2115
Epoch 14/100
7/7 [==============================] - 0s 12ms/step - loss: 2.0335 - accuracy: 0.3179 - recall: 0.0839 - precision: 0.6505 - AUROC: 0.7273 - AUPRC: 0.2933 - f1_score: 0.1486 - balanced_accuracy: 0.5394 - specificity: 0.9950 - miss_rate: 0.9161 - fall_out: 0.0050 - mcc: 0.2097 - val_loss: 1.8781 - val_accuracy: 0.3450 - val_recall: 0.0600 - val_precision: 0.8571 - val_AUROC: 0.8067 - val_AUPRC: 0.3978 - val_f1_score: 0.1121 - val_balanced_accuracy: 0.5294 - val_specificity: 0.9989 - val_miss_rate: 0.9400 - val_fall_out: 0.0011 - val_mcc: 0.2119
Epoch 15/100
7/7 [==============================] - 0s 12ms/step - loss: 1.9588 - accuracy: 0.3079 - recall: 0.0776 - precision: 0.6139 - AUROC: 0.7479 - AUPRC: 0.3047 - f1_score: 0.1378 - balanced_accuracy: 0.5361 - specificity: 0.9946 - miss_rate: 0.9224 - fall_out: 0.0054 - mcc: 0.1938 - val_loss: 1.8429 - val_accuracy: 0.3400 - val_recall: 0.0750 - val_precision: 0.8333 - val_AUROC: 0.8114 - val_AUPRC: 0.4063 - val_f1_score: 0.1376 - val_balanced_accuracy: 0.5367 - val_specificity: 0.9983 - val_miss_rate: 0.9250 - val_fall_out: 0.0017 - val_mcc: 0.2330
Epoch 16/100
7/7 [==============================] - 0s 12ms/step - loss: 1.9405 - accuracy: 0.3129 - recall: 0.1076 - precision: 0.6825 - AUROC: 0.7559 - AUPRC: 0.3227 - f1_score: 0.1859 - balanced_accuracy: 0.5510 - specificity: 0.9944 - miss_rate: 0.8924 - fall_out: 0.0056 - mcc: 0.2458 - val_loss: 1.8009 - val_accuracy: 0.3550 - val_recall: 0.0950 - val_precision: 0.8261 - val_AUROC: 0.8186 - val_AUPRC: 0.4189 - val_f1_score: 0.1704 - val_balanced_accuracy: 0.5464 - val_specificity: 0.9978 - val_miss_rate: 0.9050 - val_fall_out: 0.0022 - val_mcc: 0.2611
Epoch 17/100
7/7 [==============================] - 0s 12ms/step - loss: 1.9563 - accuracy: 0.3242 - recall: 0.1139 - precision: 0.6026 - AUROC: 0.7524 - AUPRC: 0.3138 - f1_score: 0.1916 - balanced_accuracy: 0.5528 - specificity: 0.9917 - miss_rate: 0.8861 - fall_out: 0.0083 - mcc: 0.2325 - val_loss: 1.7710 - val_accuracy: 0.3950 - val_recall: 0.1000 - val_precision: 0.8696 - val_AUROC: 0.8306 - val_AUPRC: 0.4377 - val_f1_score: 0.1794 - val_balanced_accuracy: 0.5492 - val_specificity: 0.9983 - val_miss_rate: 0.9000 - val_fall_out: 0.0017 - val_mcc: 0.2767
Epoch 18/100
7/7 [==============================] - 0s 12ms/step - loss: 1.9080 - accuracy: 0.3367 - recall: 0.1189 - precision: 0.6835 - AUROC: 0.7764 - AUPRC: 0.3490 - f1_score: 0.2026 - balanced_accuracy: 0.5564 - specificity: 0.9939 - miss_rate: 0.8811 - fall_out: 0.0061 - mcc: 0.2588 - val_loss: 1.7424 - val_accuracy: 0.4150 - val_recall: 0.1050 - val_precision: 0.8750 - val_AUROC: 0.8389 - val_AUPRC: 0.4526 - val_f1_score: 0.1875 - val_balanced_accuracy: 0.5517 - val_specificity: 0.9983 - val_miss_rate: 0.8950 - val_fall_out: 0.0017 - val_mcc: 0.2847
Epoch 19/100
7/7 [==============================] - 0s 12ms/step - loss: 1.8631 - accuracy: 0.3567 - recall: 0.1114 - precision: 0.6224 - AUROC: 0.7876 - AUPRC: 0.3541 - f1_score: 0.1890 - balanced_accuracy: 0.5519 - specificity: 0.9925 - miss_rate: 0.8886 - fall_out: 0.0075 - mcc: 0.2351 - val_loss: 1.7065 - val_accuracy: 0.3900 - val_recall: 0.1350 - val_precision: 0.8710 - val_AUROC: 0.8463 - val_AUPRC: 0.4671 - val_f1_score: 0.2338 - val_balanced_accuracy: 0.5664 - val_specificity: 0.9978 - val_miss_rate: 0.8650 - val_fall_out: 0.0022 - val_mcc: 0.3225
Epoch 20/100
7/7 [==============================] - 0s 12ms/step - loss: 1.8720 - accuracy: 0.3567 - recall: 0.1327 - precision: 0.6752 - AUROC: 0.7863 - AUPRC: 0.3577 - f1_score: 0.2218 - balanced_accuracy: 0.5628 - specificity: 0.9929 - miss_rate: 0.8673 - fall_out: 0.0071 - mcc: 0.2714 - val_loss: 1.6808 - val_accuracy: 0.3900 - val_recall: 0.1450 - val_precision: 0.8788 - val_AUROC: 0.8546 - val_AUPRC: 0.4810 - val_f1_score: 0.2489 - val_balanced_accuracy: 0.5714 - val_specificity: 0.9978 - val_miss_rate: 0.8550 - val_fall_out: 0.0022 - val_mcc: 0.3362
Epoch 21/100
7/7 [==============================] - 0s 12ms/step - loss: 1.8047 - accuracy: 0.3730 - recall: 0.1539 - precision: 0.6910 - AUROC: 0.7994 - AUPRC: 0.3837 - f1_score: 0.2518 - balanced_accuracy: 0.5731 - specificity: 0.9924 - miss_rate: 0.8461 - fall_out: 0.0076 - mcc: 0.2974 - val_loss: 1.6463 - val_accuracy: 0.3800 - val_recall: 0.1800 - val_precision: 0.8780 - val_AUROC: 0.8642 - val_AUPRC: 0.4918 - val_f1_score: 0.2988 - val_balanced_accuracy: 0.5886 - val_specificity: 0.9972 - val_miss_rate: 0.8200 - val_fall_out: 0.0028 - val_mcc: 0.3752
Epoch 22/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7968 - accuracy: 0.3692 - recall: 0.1302 - precision: 0.6265 - AUROC: 0.8035 - AUPRC: 0.3659 - f1_score: 0.2155 - balanced_accuracy: 0.5608 - specificity: 0.9914 - miss_rate: 0.8698 - fall_out: 0.0086 - mcc: 0.2556 - val_loss: 1.6213 - val_accuracy: 0.3850 - val_recall: 0.1950 - val_precision: 0.8478 - val_AUROC: 0.8707 - val_AUPRC: 0.5019 - val_f1_score: 0.3171 - val_balanced_accuracy: 0.5956 - val_specificity: 0.9961 - val_miss_rate: 0.8050 - val_fall_out: 0.0039 - val_mcc: 0.3825
Epoch 23/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7577 - accuracy: 0.3905 - recall: 0.1640 - precision: 0.7198 - AUROC: 0.8211 - AUPRC: 0.4219 - f1_score: 0.2671 - balanced_accuracy: 0.5784 - specificity: 0.9929 - miss_rate: 0.8360 - fall_out: 0.0071 - mcc: 0.3154 - val_loss: 1.5960 - val_accuracy: 0.3850 - val_recall: 0.2000 - val_precision: 0.8511 - val_AUROC: 0.8773 - val_AUPRC: 0.5072 - val_f1_score: 0.3239 - val_balanced_accuracy: 0.5981 - val_specificity: 0.9961 - val_miss_rate: 0.8000 - val_fall_out: 0.0039 - val_mcc: 0.3884
Epoch 24/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7929 - accuracy: 0.3755 - recall: 0.1502 - precision: 0.6417 - AUROC: 0.8069 - AUPRC: 0.3812 - f1_score: 0.2434 - balanced_accuracy: 0.5704 - specificity: 0.9907 - miss_rate: 0.8498 - fall_out: 0.0093 - mcc: 0.2795 - val_loss: 1.5670 - val_accuracy: 0.4100 - val_recall: 0.2150 - val_precision: 0.8431 - val_AUROC: 0.8832 - val_AUPRC: 0.5238 - val_f1_score: 0.3426 - val_balanced_accuracy: 0.6053 - val_specificity: 0.9956 - val_miss_rate: 0.7850 - val_fall_out: 0.0044 - val_mcc: 0.4007
Epoch 25/100
7/7 [==============================] - 0s 12ms/step - loss: 1.6854 - accuracy: 0.4055 - recall: 0.1552 - precision: 0.6739 - AUROC: 0.8359 - AUPRC: 0.4289 - f1_score: 0.2523 - balanced_accuracy: 0.5734 - specificity: 0.9917 - miss_rate: 0.8448 - fall_out: 0.0083 - mcc: 0.2937 - val_loss: 1.5376 - val_accuracy: 0.4100 - val_recall: 0.2350 - val_precision: 0.8393 - val_AUROC: 0.8867 - val_AUPRC: 0.5340 - val_f1_score: 0.3672 - val_balanced_accuracy: 0.6150 - val_specificity: 0.9950 - val_miss_rate: 0.7650 - val_fall_out: 0.0050 - val_mcc: 0.4183
Epoch 26/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7405 - accuracy: 0.3905 - recall: 0.1827 - precision: 0.6460 - AUROC: 0.8232 - AUPRC: 0.4121 - f1_score: 0.2849 - balanced_accuracy: 0.5858 - specificity: 0.9889 - miss_rate: 0.8173 - fall_out: 0.0111 - mcc: 0.3105 - val_loss: 1.5233 - val_accuracy: 0.4100 - val_recall: 0.2450 - val_precision: 0.8305 - val_AUROC: 0.8883 - val_AUPRC: 0.5374 - val_f1_score: 0.3784 - val_balanced_accuracy: 0.6197 - val_specificity: 0.9944 - val_miss_rate: 0.7550 - val_fall_out: 0.0056 - val_mcc: 0.4245
Epoch 27/100
7/7 [==============================] - 0s 12ms/step - loss: 1.6781 - accuracy: 0.4093 - recall: 0.2090 - precision: 0.7076 - AUROC: 0.8364 - AUPRC: 0.4463 - f1_score: 0.3227 - balanced_accuracy: 0.5997 - specificity: 0.9904 - miss_rate: 0.7910 - fall_out: 0.0096 - mcc: 0.3534 - val_loss: 1.5046 - val_accuracy: 0.4200 - val_recall: 0.2500 - val_precision: 0.8772 - val_AUROC: 0.8922 - val_AUPRC: 0.5453 - val_f1_score: 0.3891 - val_balanced_accuracy: 0.6231 - val_specificity: 0.9961 - val_miss_rate: 0.7500 - val_fall_out: 0.0039 - val_mcc: 0.4437
Epoch 28/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7038 - accuracy: 0.3855 - recall: 0.1727 - precision: 0.6509 - AUROC: 0.8359 - AUPRC: 0.4267 - f1_score: 0.2730 - balanced_accuracy: 0.5812 - specificity: 0.9897 - miss_rate: 0.8273 - fall_out: 0.0103 - mcc: 0.3032 - val_loss: 1.4904 - val_accuracy: 0.4300 - val_recall: 0.2550 - val_precision: 0.8500 - val_AUROC: 0.8940 - val_AUPRC: 0.5493 - val_f1_score: 0.3923 - val_balanced_accuracy: 0.6250 - val_specificity: 0.9950 - val_miss_rate: 0.7450 - val_fall_out: 0.0050 - val_mcc: 0.4397
Epoch 29/100
7/7 [==============================] - 0s 12ms/step - loss: 1.6466 - accuracy: 0.4143 - recall: 0.1927 - precision: 0.6754 - AUROC: 0.8473 - AUPRC: 0.4483 - f1_score: 0.2999 - balanced_accuracy: 0.5912 - specificity: 0.9897 - miss_rate: 0.8073 - fall_out: 0.0103 - mcc: 0.3287 - val_loss: 1.4659 - val_accuracy: 0.4400 - val_recall: 0.2650 - val_precision: 0.8413 - val_AUROC: 0.8975 - val_AUPRC: 0.5602 - val_f1_score: 0.4030 - val_balanced_accuracy: 0.6297 - val_specificity: 0.9944 - val_miss_rate: 0.7350 - val_fall_out: 0.0056 - val_mcc: 0.4456
Epoch 30/100
7/7 [==============================] - 0s 13ms/step - loss: 1.6479 - accuracy: 0.4280 - recall: 0.1865 - precision: 0.6535 - AUROC: 0.8444 - AUPRC: 0.4542 - f1_score: 0.2902 - balanced_accuracy: 0.5877 - specificity: 0.9890 - miss_rate: 0.8135 - fall_out: 0.0110 - mcc: 0.3162 - val_loss: 1.4504 - val_accuracy: 0.4400 - val_recall: 0.2700 - val_precision: 0.8571 - val_AUROC: 0.8995 - val_AUPRC: 0.5657 - val_f1_score: 0.4106 - val_balanced_accuracy: 0.6325 - val_specificity: 0.9950 - val_miss_rate: 0.7300 - val_fall_out: 0.0050 - val_mcc: 0.4552
Epoch 31/100
7/7 [==============================] - 0s 14ms/step - loss: 1.6454 - accuracy: 0.4318 - recall: 0.2015 - precision: 0.6793 - AUROC: 0.8466 - AUPRC: 0.4553 - f1_score: 0.3108 - balanced_accuracy: 0.5955 - specificity: 0.9894 - miss_rate: 0.7985 - fall_out: 0.0106 - mcc: 0.3376 - val_loss: 1.4319 - val_accuracy: 0.4600 - val_recall: 0.2650 - val_precision: 0.8281 - val_AUROC: 0.9017 - val_AUPRC: 0.5719 - val_f1_score: 0.4015 - val_balanced_accuracy: 0.6294 - val_specificity: 0.9939 - val_miss_rate: 0.7350 - val_fall_out: 0.0061 - val_mcc: 0.4413
Epoch 32/100
7/7 [==============================] - 0s 13ms/step - loss: 1.5643 - accuracy: 0.4493 - recall: 0.2278 - precision: 0.6868 - AUROC: 0.8615 - AUPRC: 0.4824 - f1_score: 0.3421 - balanced_accuracy: 0.6081 - specificity: 0.9885 - miss_rate: 0.7722 - fall_out: 0.0115 - mcc: 0.3623 - val_loss: 1.4114 - val_accuracy: 0.4800 - val_recall: 0.2750 - val_precision: 0.8333 - val_AUROC: 0.9036 - val_AUPRC: 0.5813 - val_f1_score: 0.4135 - val_balanced_accuracy: 0.6344 - val_specificity: 0.9939 - val_miss_rate: 0.7250 - val_fall_out: 0.0061 - val_mcc: 0.4516
Epoch 33/100
7/7 [==============================] - 0s 12ms/step - loss: 1.5697 - accuracy: 0.4518 - recall: 0.2228 - precision: 0.6794 - AUROC: 0.8606 - AUPRC: 0.4796 - f1_score: 0.3355 - balanced_accuracy: 0.6055 - specificity: 0.9883 - miss_rate: 0.7772 - fall_out: 0.0117 - mcc: 0.3556 - val_loss: 1.3921 - val_accuracy: 0.5050 - val_recall: 0.2800 - val_precision: 0.8358 - val_AUROC: 0.9069 - val_AUPRC: 0.5909 - val_f1_score: 0.4195 - val_balanced_accuracy: 0.6369 - val_specificity: 0.9939 - val_miss_rate: 0.7200 - val_fall_out: 0.0061 - val_mcc: 0.4566
Epoch 34/100
7/7 [==============================] - 0s 12ms/step - loss: 1.5348 - accuracy: 0.4355 - recall: 0.2278 - precision: 0.6868 - AUROC: 0.8683 - AUPRC: 0.4856 - f1_score: 0.3421 - balanced_accuracy: 0.6081 - specificity: 0.9885 - miss_rate: 0.7722 - fall_out: 0.0115 - mcc: 0.3623 - val_loss: 1.3753 - val_accuracy: 0.4950 - val_recall: 0.2800 - val_precision: 0.8485 - val_AUROC: 0.9098 - val_AUPRC: 0.5931 - val_f1_score: 0.4211 - val_balanced_accuracy: 0.6372 - val_specificity: 0.9944 - val_miss_rate: 0.7200 - val_fall_out: 0.0056 - val_mcc: 0.4609
Epoch 35/100
7/7 [==============================] - 0s 12ms/step - loss: 1.5647 - accuracy: 0.4593 - recall: 0.2315 - precision: 0.7008 - AUROC: 0.8683 - AUPRC: 0.4940 - f1_score: 0.3481 - balanced_accuracy: 0.6103 - specificity: 0.9890 - miss_rate: 0.7685 - fall_out: 0.0110 - mcc: 0.3702 - val_loss: 1.3520 - val_accuracy: 0.5150 - val_recall: 0.2900 - val_precision: 0.8529 - val_AUROC: 0.9128 - val_AUPRC: 0.6027 - val_f1_score: 0.4328 - val_balanced_accuracy: 0.6422 - val_specificity: 0.9944 - val_miss_rate: 0.7100 - val_fall_out: 0.0056 - val_mcc: 0.4709
Epoch 36/100
7/7 [==============================] - 0s 12ms/step - loss: 1.5235 - accuracy: 0.4506 - recall: 0.2290 - precision: 0.6728 - AUROC: 0.8689 - AUPRC: 0.4920 - f1_score: 0.3417 - balanced_accuracy: 0.6083 - specificity: 0.9876 - miss_rate: 0.7710 - fall_out: 0.0124 - mcc: 0.3584 - val_loss: 1.3363 - val_accuracy: 0.5250 - val_recall: 0.2950 - val_precision: 0.8310 - val_AUROC: 0.9135 - val_AUPRC: 0.6075 - val_f1_score: 0.4354 - val_balanced_accuracy: 0.6442 - val_specificity: 0.9933 - val_miss_rate: 0.7050 - val_fall_out: 0.0067 - val_mcc: 0.4675
Epoch 37/100
7/7 [==============================] - 0s 12ms/step - loss: 1.5161 - accuracy: 0.4418 - recall: 0.2441 - precision: 0.7276 - AUROC: 0.8722 - AUPRC: 0.5060 - f1_score: 0.3655 - balanced_accuracy: 0.6170 - specificity: 0.9898 - miss_rate: 0.7559 - fall_out: 0.0102 - mcc: 0.3897 - val_loss: 1.3220 - val_accuracy: 0.5350 - val_recall: 0.3000 - val_precision: 0.8333 - val_AUROC: 0.9152 - val_AUPRC: 0.6152 - val_f1_score: 0.4412 - val_balanced_accuracy: 0.6467 - val_specificity: 0.9933 - val_miss_rate: 0.7000 - val_fall_out: 0.0067 - val_mcc: 0.4724
Epoch 38/100
7/7 [==============================] - 0s 13ms/step - loss: 1.4814 - accuracy: 0.4631 - recall: 0.2616 - precision: 0.6830 - AUROC: 0.8806 - AUPRC: 0.5083 - f1_score: 0.3783 - balanced_accuracy: 0.6240 - specificity: 0.9865 - miss_rate: 0.7384 - fall_out: 0.0135 - mcc: 0.3878 - val_loss: 1.3119 - val_accuracy: 0.5450 - val_recall: 0.2850 - val_precision: 0.8261 - val_AUROC: 0.9164 - val_AUPRC: 0.6211 - val_f1_score: 0.4238 - val_balanced_accuracy: 0.6392 - val_specificity: 0.9933 - val_miss_rate: 0.7150 - val_fall_out: 0.0067 - val_mcc: 0.4575
Epoch 39/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4643 - accuracy: 0.4906 - recall: 0.2553 - precision: 0.7034 - AUROC: 0.8827 - AUPRC: 0.5180 - f1_score: 0.3747 - balanced_accuracy: 0.6217 - specificity: 0.9880 - miss_rate: 0.7447 - fall_out: 0.0120 - mcc: 0.3904 - val_loss: 1.2949 - val_accuracy: 0.5550 - val_recall: 0.2900 - val_precision: 0.8529 - val_AUROC: 0.9180 - val_AUPRC: 0.6320 - val_f1_score: 0.4328 - val_balanced_accuracy: 0.6422 - val_specificity: 0.9944 - val_miss_rate: 0.7100 - val_fall_out: 0.0056 - val_mcc: 0.4709
Epoch 40/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4562 - accuracy: 0.4731 - recall: 0.2340 - precision: 0.6538 - AUROC: 0.8836 - AUPRC: 0.5131 - f1_score: 0.3447 - balanced_accuracy: 0.6101 - specificity: 0.9862 - miss_rate: 0.7660 - fall_out: 0.0138 - mcc: 0.3557 - val_loss: 1.2770 - val_accuracy: 0.5600 - val_recall: 0.2950 - val_precision: 0.8429 - val_AUROC: 0.9206 - val_AUPRC: 0.6367 - val_f1_score: 0.4370 - val_balanced_accuracy: 0.6444 - val_specificity: 0.9939 - val_miss_rate: 0.7050 - val_fall_out: 0.0061 - val_mcc: 0.4716
Epoch 41/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4179 - accuracy: 0.4931 - recall: 0.2553 - precision: 0.7208 - AUROC: 0.8881 - AUPRC: 0.5452 - f1_score: 0.3771 - balanced_accuracy: 0.6222 - specificity: 0.9890 - miss_rate: 0.7447 - fall_out: 0.0110 - mcc: 0.3966 - val_loss: 1.2671 - val_accuracy: 0.5750 - val_recall: 0.3050 - val_precision: 0.8356 - val_AUROC: 0.9208 - val_AUPRC: 0.6379 - val_f1_score: 0.4469 - val_balanced_accuracy: 0.6492 - val_specificity: 0.9933 - val_miss_rate: 0.6950 - val_fall_out: 0.0067 - val_mcc: 0.4773
Epoch 42/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3866 - accuracy: 0.4931 - recall: 0.2916 - precision: 0.7397 - AUROC: 0.8937 - AUPRC: 0.5546 - f1_score: 0.4183 - balanced_accuracy: 0.6401 - specificity: 0.9886 - miss_rate: 0.7084 - fall_out: 0.0114 - mcc: 0.4320 - val_loss: 1.2602 - val_accuracy: 0.5550 - val_recall: 0.3200 - val_precision: 0.8421 - val_AUROC: 0.9216 - val_AUPRC: 0.6349 - val_f1_score: 0.4638 - val_balanced_accuracy: 0.6567 - val_specificity: 0.9933 - val_miss_rate: 0.6800 - val_fall_out: 0.0067 - val_mcc: 0.4916
Epoch 43/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4688 - accuracy: 0.4643 - recall: 0.2628 - precision: 0.6731 - AUROC: 0.8780 - AUPRC: 0.5072 - f1_score: 0.3780 - balanced_accuracy: 0.6243 - specificity: 0.9858 - miss_rate: 0.7372 - fall_out: 0.0142 - mcc: 0.3851 - val_loss: 1.2510 - val_accuracy: 0.5650 - val_recall: 0.3100 - val_precision: 0.8378 - val_AUROC: 0.9229 - val_AUPRC: 0.6410 - val_f1_score: 0.4526 - val_balanced_accuracy: 0.6517 - val_specificity: 0.9933 - val_miss_rate: 0.6900 - val_fall_out: 0.0067 - val_mcc: 0.4821
Epoch 44/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3853 - accuracy: 0.5031 - recall: 0.2829 - precision: 0.7197 - AUROC: 0.8942 - AUPRC: 0.5610 - f1_score: 0.4061 - balanced_accuracy: 0.6353 - specificity: 0.9878 - miss_rate: 0.7171 - fall_out: 0.0122 - mcc: 0.4178 - val_loss: 1.2350 - val_accuracy: 0.5850 - val_recall: 0.3100 - val_precision: 0.8611 - val_AUROC: 0.9246 - val_AUPRC: 0.6513 - val_f1_score: 0.4559 - val_balanced_accuracy: 0.6522 - val_specificity: 0.9944 - val_miss_rate: 0.6900 - val_fall_out: 0.0056 - val_mcc: 0.4903
Epoch 45/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3602 - accuracy: 0.5044 - recall: 0.2866 - precision: 0.7134 - AUROC: 0.9001 - AUPRC: 0.5628 - f1_score: 0.4089 - balanced_accuracy: 0.6369 - specificity: 0.9872 - miss_rate: 0.7134 - fall_out: 0.0128 - mcc: 0.4183 - val_loss: 1.2138 - val_accuracy: 0.5900 - val_recall: 0.3050 - val_precision: 0.8592 - val_AUROC: 0.9268 - val_AUPRC: 0.6616 - val_f1_score: 0.4502 - val_balanced_accuracy: 0.6497 - val_specificity: 0.9944 - val_miss_rate: 0.6950 - val_fall_out: 0.0056 - val_mcc: 0.4855
Epoch 46/100
7/7 [==============================] - 0s 13ms/step - loss: 1.4047 - accuracy: 0.5006 - recall: 0.2816 - precision: 0.6902 - AUROC: 0.8898 - AUPRC: 0.5459 - f1_score: 0.4000 - balanced_accuracy: 0.6338 - specificity: 0.9860 - miss_rate: 0.7184 - fall_out: 0.0140 - mcc: 0.4057 - val_loss: 1.1982 - val_accuracy: 0.5950 - val_recall: 0.3250 - val_precision: 0.8784 - val_AUROC: 0.9287 - val_AUPRC: 0.6677 - val_f1_score: 0.4745 - val_balanced_accuracy: 0.6600 - val_specificity: 0.9950 - val_miss_rate: 0.6750 - val_fall_out: 0.0050 - val_mcc: 0.5086
Epoch 47/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3753 - accuracy: 0.5119 - recall: 0.2929 - precision: 0.6985 - AUROC: 0.8963 - AUPRC: 0.5611 - f1_score: 0.4127 - balanced_accuracy: 0.6394 - specificity: 0.9860 - miss_rate: 0.7071 - fall_out: 0.0140 - mcc: 0.4173 - val_loss: 1.1872 - val_accuracy: 0.6050 - val_recall: 0.3200 - val_precision: 0.8649 - val_AUROC: 0.9300 - val_AUPRC: 0.6753 - val_f1_score: 0.4672 - val_balanced_accuracy: 0.6572 - val_specificity: 0.9944 - val_miss_rate: 0.6800 - val_fall_out: 0.0056 - val_mcc: 0.4997
Epoch 48/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3164 - accuracy: 0.5181 - recall: 0.2929 - precision: 0.7112 - AUROC: 0.9042 - AUPRC: 0.5719 - f1_score: 0.4149 - balanced_accuracy: 0.6398 - specificity: 0.9868 - miss_rate: 0.7071 - fall_out: 0.0132 - mcc: 0.4222 - val_loss: 1.1811 - val_accuracy: 0.6150 - val_recall: 0.3150 - val_precision: 0.8514 - val_AUROC: 0.9312 - val_AUPRC: 0.6780 - val_f1_score: 0.4599 - val_balanced_accuracy: 0.6544 - val_specificity: 0.9939 - val_miss_rate: 0.6850 - val_fall_out: 0.0061 - val_mcc: 0.4909
Epoch 49/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3396 - accuracy: 0.5194 - recall: 0.2941 - precision: 0.7209 - AUROC: 0.9004 - AUPRC: 0.5637 - f1_score: 0.4178 - balanced_accuracy: 0.6407 - specificity: 0.9873 - miss_rate: 0.7059 - fall_out: 0.0127 - mcc: 0.4268 - val_loss: 1.1740 - val_accuracy: 0.6200 - val_recall: 0.3300 - val_precision: 0.8684 - val_AUROC: 0.9312 - val_AUPRC: 0.6746 - val_f1_score: 0.4783 - val_balanced_accuracy: 0.6622 - val_specificity: 0.9944 - val_miss_rate: 0.6700 - val_fall_out: 0.0056 - val_mcc: 0.5091
Epoch 50/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3129 - accuracy: 0.5307 - recall: 0.3229 - precision: 0.7127 - AUROC: 0.9055 - AUPRC: 0.5899 - f1_score: 0.4444 - balanced_accuracy: 0.6542 - specificity: 0.9855 - miss_rate: 0.6771 - fall_out: 0.0145 - mcc: 0.4449 - val_loss: 1.1560 - val_accuracy: 0.6100 - val_recall: 0.3350 - val_precision: 0.8590 - val_AUROC: 0.9333 - val_AUPRC: 0.6843 - val_f1_score: 0.4820 - val_balanced_accuracy: 0.6644 - val_specificity: 0.9939 - val_miss_rate: 0.6650 - val_fall_out: 0.0061 - val_mcc: 0.5097
Epoch 51/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3586 - accuracy: 0.5006 - recall: 0.2866 - precision: 0.6918 - AUROC: 0.9024 - AUPRC: 0.5625 - f1_score: 0.4053 - balanced_accuracy: 0.6362 - specificity: 0.9858 - miss_rate: 0.7134 - fall_out: 0.0142 - mcc: 0.4101 - val_loss: 1.1433 - val_accuracy: 0.6200 - val_recall: 0.3350 - val_precision: 0.8816 - val_AUROC: 0.9353 - val_AUPRC: 0.6927 - val_f1_score: 0.4855 - val_balanced_accuracy: 0.6650 - val_specificity: 0.9950 - val_miss_rate: 0.6650 - val_fall_out: 0.0050 - val_mcc: 0.5178
Epoch 52/100
7/7 [==============================] - 0s 13ms/step - loss: 1.3179 - accuracy: 0.5169 - recall: 0.3104 - precision: 0.7106 - AUROC: 0.9060 - AUPRC: 0.5766 - f1_score: 0.4321 - balanced_accuracy: 0.6482 - specificity: 0.9860 - miss_rate: 0.6896 - fall_out: 0.0140 - mcc: 0.4350 - val_loss: 1.1370 - val_accuracy: 0.6300 - val_recall: 0.3250 - val_precision: 0.8667 - val_AUROC: 0.9358 - val_AUPRC: 0.6935 - val_f1_score: 0.4727 - val_balanced_accuracy: 0.6597 - val_specificity: 0.9944 - val_miss_rate: 0.6750 - val_fall_out: 0.0056 - val_mcc: 0.5044
Epoch 53/100
7/7 [==============================] - 0s 15ms/step - loss: 1.3128 - accuracy: 0.5357 - recall: 0.3141 - precision: 0.7011 - AUROC: 0.9038 - AUPRC: 0.5789 - f1_score: 0.4339 - balanced_accuracy: 0.6496 - specificity: 0.9851 - miss_rate: 0.6859 - fall_out: 0.0149 - mcc: 0.4340 - val_loss: 1.1271 - val_accuracy: 0.6300 - val_recall: 0.3350 - val_precision: 0.8816 - val_AUROC: 0.9380 - val_AUPRC: 0.7025 - val_f1_score: 0.4855 - val_balanced_accuracy: 0.6650 - val_specificity: 0.9950 - val_miss_rate: 0.6650 - val_fall_out: 0.0050 - val_mcc: 0.5178
Epoch 54/100
7/7 [==============================] - 0s 13ms/step - loss: 1.2710 - accuracy: 0.5569 - recall: 0.3242 - precision: 0.7096 - AUROC: 0.9127 - AUPRC: 0.6015 - f1_score: 0.4450 - balanced_accuracy: 0.6547 - specificity: 0.9853 - miss_rate: 0.6758 - fall_out: 0.0147 - mcc: 0.4446 - val_loss: 1.1116 - val_accuracy: 0.6400 - val_recall: 0.3500 - val_precision: 0.8861 - val_AUROC: 0.9389 - val_AUPRC: 0.7102 - val_f1_score: 0.5018 - val_balanced_accuracy: 0.6725 - val_specificity: 0.9950 - val_miss_rate: 0.6500 - val_fall_out: 0.0050 - val_mcc: 0.5314
Epoch 55/100
7/7 [==============================] - 0s 13ms/step - loss: 1.2852 - accuracy: 0.5294 - recall: 0.3217 - precision: 0.7471 - AUROC: 0.9114 - AUPRC: 0.5900 - f1_score: 0.4497 - balanced_accuracy: 0.6548 - specificity: 0.9879 - miss_rate: 0.6783 - fall_out: 0.0121 - mcc: 0.4575 - val_loss: 1.1022 - val_accuracy: 0.6550 - val_recall: 0.3650 - val_precision: 0.8795 - val_AUROC: 0.9400 - val_AUPRC: 0.7157 - val_f1_score: 0.5159 - val_balanced_accuracy: 0.6797 - val_specificity: 0.9944 - val_miss_rate: 0.6350 - val_fall_out: 0.0056 - val_mcc: 0.5407
Epoch 56/100
7/7 [==============================] - 0s 13ms/step - loss: 1.2237 - accuracy: 0.5494 - recall: 0.3479 - precision: 0.7514 - AUROC: 0.9183 - AUPRC: 0.6185 - f1_score: 0.4756 - balanced_accuracy: 0.6676 - specificity: 0.9872 - miss_rate: 0.6521 - fall_out: 0.0128 - mcc: 0.4784 - val_loss: 1.0910 - val_accuracy: 0.6500 - val_recall: 0.3550 - val_precision: 0.8875 - val_AUROC: 0.9409 - val_AUPRC: 0.7209 - val_f1_score: 0.5071 - val_balanced_accuracy: 0.6750 - val_specificity: 0.9950 - val_miss_rate: 0.6450 - val_fall_out: 0.0050 - val_mcc: 0.5358
Epoch 57/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2616 - accuracy: 0.5682 - recall: 0.3492 - precision: 0.7686 - AUROC: 0.9137 - AUPRC: 0.6239 - f1_score: 0.4802 - balanced_accuracy: 0.6688 - specificity: 0.9883 - miss_rate: 0.6508 - fall_out: 0.0117 - mcc: 0.4862 - val_loss: 1.0766 - val_accuracy: 0.6400 - val_recall: 0.3900 - val_precision: 0.9070 - val_AUROC: 0.9418 - val_AUPRC: 0.7270 - val_f1_score: 0.5455 - val_balanced_accuracy: 0.6928 - val_specificity: 0.9956 - val_miss_rate: 0.6100 - val_fall_out: 0.0044 - val_mcc: 0.5702
Epoch 58/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2438 - accuracy: 0.5432 - recall: 0.3442 - precision: 0.7161 - AUROC: 0.9166 - AUPRC: 0.5974 - f1_score: 0.4649 - balanced_accuracy: 0.6645 - specificity: 0.9848 - miss_rate: 0.6558 - fall_out: 0.0152 - mcc: 0.4615 - val_loss: 1.0616 - val_accuracy: 0.6350 - val_recall: 0.4050 - val_precision: 0.9000 - val_AUROC: 0.9434 - val_AUPRC: 0.7297 - val_f1_score: 0.5586 - val_balanced_accuracy: 0.7000 - val_specificity: 0.9950 - val_miss_rate: 0.5950 - val_fall_out: 0.0050 - val_mcc: 0.5789
Epoch 59/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2385 - accuracy: 0.5695 - recall: 0.3492 - precision: 0.7063 - AUROC: 0.9163 - AUPRC: 0.6218 - f1_score: 0.4673 - balanced_accuracy: 0.6665 - specificity: 0.9839 - miss_rate: 0.6508 - fall_out: 0.0161 - mcc: 0.4609 - val_loss: 1.0582 - val_accuracy: 0.6400 - val_recall: 0.4150 - val_precision: 0.8925 - val_AUROC: 0.9432 - val_AUPRC: 0.7313 - val_f1_score: 0.5666 - val_balanced_accuracy: 0.7047 - val_specificity: 0.9944 - val_miss_rate: 0.5850 - val_fall_out: 0.0056 - val_mcc: 0.5834
Epoch 60/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2123 - accuracy: 0.5682 - recall: 0.3642 - precision: 0.7275 - AUROC: 0.9199 - AUPRC: 0.6253 - f1_score: 0.4854 - balanced_accuracy: 0.6745 - specificity: 0.9848 - miss_rate: 0.6358 - fall_out: 0.0152 - mcc: 0.4802 - val_loss: 1.0464 - val_accuracy: 0.6450 - val_recall: 0.4150 - val_precision: 0.8830 - val_AUROC: 0.9442 - val_AUPRC: 0.7367 - val_f1_score: 0.5646 - val_balanced_accuracy: 0.7044 - val_specificity: 0.9939 - val_miss_rate: 0.5850 - val_fall_out: 0.0061 - val_mcc: 0.5796
Epoch 61/100
7/7 [==============================] - 0s 13ms/step - loss: 1.2325 - accuracy: 0.5732 - recall: 0.3342 - precision: 0.6935 - AUROC: 0.9163 - AUPRC: 0.6234 - f1_score: 0.4510 - balanced_accuracy: 0.6589 - specificity: 0.9836 - miss_rate: 0.6658 - fall_out: 0.0164 - mcc: 0.4451 - val_loss: 1.0344 - val_accuracy: 0.6550 - val_recall: 0.4300 - val_precision: 0.9149 - val_AUROC: 0.9465 - val_AUPRC: 0.7431 - val_f1_score: 0.5850 - val_balanced_accuracy: 0.7128 - val_specificity: 0.9956 - val_miss_rate: 0.5700 - val_fall_out: 0.0044 - val_mcc: 0.6032
Epoch 62/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2120 - accuracy: 0.5707 - recall: 0.3730 - precision: 0.7358 - AUROC: 0.9178 - AUPRC: 0.6212 - f1_score: 0.4950 - balanced_accuracy: 0.6790 - specificity: 0.9851 - miss_rate: 0.6270 - fall_out: 0.0149 - mcc: 0.4897 - val_loss: 1.0312 - val_accuracy: 0.6750 - val_recall: 0.4300 - val_precision: 0.9149 - val_AUROC: 0.9464 - val_AUPRC: 0.7428 - val_f1_score: 0.5850 - val_balanced_accuracy: 0.7128 - val_specificity: 0.9956 - val_miss_rate: 0.5700 - val_fall_out: 0.0044 - val_mcc: 0.6032
Epoch 63/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1654 - accuracy: 0.5820 - recall: 0.3767 - precision: 0.7620 - AUROC: 0.9255 - AUPRC: 0.6561 - f1_score: 0.5042 - balanced_accuracy: 0.6818 - specificity: 0.9869 - miss_rate: 0.6233 - fall_out: 0.0131 - mcc: 0.5033 - val_loss: 1.0294 - val_accuracy: 0.6600 - val_recall: 0.4350 - val_precision: 0.9158 - val_AUROC: 0.9455 - val_AUPRC: 0.7432 - val_f1_score: 0.5898 - val_balanced_accuracy: 0.7153 - val_specificity: 0.9956 - val_miss_rate: 0.5650 - val_fall_out: 0.0044 - val_mcc: 0.6073
Epoch 64/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1978 - accuracy: 0.5832 - recall: 0.3717 - precision: 0.7538 - AUROC: 0.9218 - AUPRC: 0.6437 - f1_score: 0.4979 - balanced_accuracy: 0.6791 - specificity: 0.9865 - miss_rate: 0.6283 - fall_out: 0.0135 - mcc: 0.4963 - val_loss: 1.0274 - val_accuracy: 0.6500 - val_recall: 0.4150 - val_precision: 0.8469 - val_AUROC: 0.9458 - val_AUPRC: 0.7404 - val_f1_score: 0.5570 - val_balanced_accuracy: 0.7033 - val_specificity: 0.9917 - val_miss_rate: 0.5850 - val_fall_out: 0.0083 - val_mcc: 0.5652
Epoch 65/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1899 - accuracy: 0.5857 - recall: 0.3642 - precision: 0.7275 - AUROC: 0.9218 - AUPRC: 0.6371 - f1_score: 0.4854 - balanced_accuracy: 0.6745 - specificity: 0.9848 - miss_rate: 0.6358 - fall_out: 0.0152 - mcc: 0.4802 - val_loss: 1.0198 - val_accuracy: 0.6650 - val_recall: 0.4450 - val_precision: 0.8990 - val_AUROC: 0.9466 - val_AUPRC: 0.7434 - val_f1_score: 0.5953 - val_balanced_accuracy: 0.7197 - val_specificity: 0.9944 - val_miss_rate: 0.5550 - val_fall_out: 0.0056 - val_mcc: 0.6078
Epoch 66/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1892 - accuracy: 0.5932 - recall: 0.4105 - precision: 0.7828 - AUROC: 0.9250 - AUPRC: 0.6619 - f1_score: 0.5386 - balanced_accuracy: 0.6989 - specificity: 0.9873 - miss_rate: 0.5895 - fall_out: 0.0127 - mcc: 0.5354 - val_loss: 0.9981 - val_accuracy: 0.6600 - val_recall: 0.4400 - val_precision: 0.9167 - val_AUROC: 0.9494 - val_AUPRC: 0.7572 - val_f1_score: 0.5946 - val_balanced_accuracy: 0.7178 - val_specificity: 0.9956 - val_miss_rate: 0.5600 - val_fall_out: 0.0044 - val_mcc: 0.6113
Epoch 67/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1795 - accuracy: 0.6020 - recall: 0.3767 - precision: 0.7469 - AUROC: 0.9234 - AUPRC: 0.6400 - f1_score: 0.5008 - balanced_accuracy: 0.6813 - specificity: 0.9858 - miss_rate: 0.6233 - fall_out: 0.0142 - mcc: 0.4970 - val_loss: 0.9949 - val_accuracy: 0.6650 - val_recall: 0.4650 - val_precision: 0.8942 - val_AUROC: 0.9497 - val_AUPRC: 0.7575 - val_f1_score: 0.6118 - val_balanced_accuracy: 0.7294 - val_specificity: 0.9939 - val_miss_rate: 0.5350 - val_fall_out: 0.0061 - val_mcc: 0.6200
Epoch 68/100
7/7 [==============================] - 0s 11ms/step - loss: 1.1273 - accuracy: 0.6083 - recall: 0.4205 - precision: 0.7636 - AUROC: 0.9317 - AUPRC: 0.6762 - f1_score: 0.5424 - balanced_accuracy: 0.7030 - specificity: 0.9855 - miss_rate: 0.5795 - fall_out: 0.0145 - mcc: 0.5340 - val_loss: 0.9928 - val_accuracy: 0.6900 - val_recall: 0.4600 - val_precision: 0.9109 - val_AUROC: 0.9499 - val_AUPRC: 0.7594 - val_f1_score: 0.6113 - val_balanced_accuracy: 0.7275 - val_specificity: 0.9950 - val_miss_rate: 0.5400 - val_fall_out: 0.0050 - val_mcc: 0.6234
Epoch 69/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1881 - accuracy: 0.5870 - recall: 0.3917 - precision: 0.7330 - AUROC: 0.9225 - AUPRC: 0.6426 - f1_score: 0.5106 - balanced_accuracy: 0.6879 - specificity: 0.9841 - miss_rate: 0.6083 - fall_out: 0.0159 - mcc: 0.5014 - val_loss: 0.9876 - val_accuracy: 0.7000 - val_recall: 0.4600 - val_precision: 0.9388 - val_AUROC: 0.9513 - val_AUPRC: 0.7661 - val_f1_score: 0.6174 - val_balanced_accuracy: 0.7283 - val_specificity: 0.9967 - val_miss_rate: 0.5400 - val_fall_out: 0.0033 - val_mcc: 0.6346
Epoch 70/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1273 - accuracy: 0.5820 - recall: 0.3855 - precision: 0.7404 - AUROC: 0.9330 - AUPRC: 0.6585 - f1_score: 0.5070 - balanced_accuracy: 0.6852 - specificity: 0.9850 - miss_rate: 0.6145 - fall_out: 0.0150 - mcc: 0.5003 - val_loss: 0.9918 - val_accuracy: 0.7000 - val_recall: 0.4500 - val_precision: 0.9375 - val_AUROC: 0.9504 - val_AUPRC: 0.7620 - val_f1_score: 0.6081 - val_balanced_accuracy: 0.7233 - val_specificity: 0.9967 - val_miss_rate: 0.5500 - val_fall_out: 0.0033 - val_mcc: 0.6269
Epoch 71/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1481 - accuracy: 0.5920 - recall: 0.4080 - precision: 0.7546 - AUROC: 0.9257 - AUPRC: 0.6608 - f1_score: 0.5297 - balanced_accuracy: 0.6966 - specificity: 0.9853 - miss_rate: 0.5920 - fall_out: 0.0147 - mcc: 0.5217 - val_loss: 0.9827 - val_accuracy: 0.6950 - val_recall: 0.4500 - val_precision: 0.9375 - val_AUROC: 0.9510 - val_AUPRC: 0.7678 - val_f1_score: 0.6081 - val_balanced_accuracy: 0.7233 - val_specificity: 0.9967 - val_miss_rate: 0.5500 - val_fall_out: 0.0033 - val_mcc: 0.6269
Epoch 72/100
7/7 [==============================] - 0s 12ms/step - loss: 1.0963 - accuracy: 0.5995 - recall: 0.4230 - precision: 0.7562 - AUROC: 0.9342 - AUPRC: 0.6792 - f1_score: 0.5425 - balanced_accuracy: 0.7039 - specificity: 0.9848 - miss_rate: 0.5770 - fall_out: 0.0152 - mcc: 0.5324 - val_loss: 0.9770 - val_accuracy: 0.6950 - val_recall: 0.4600 - val_precision: 0.9388 - val_AUROC: 0.9514 - val_AUPRC: 0.7703 - val_f1_score: 0.6174 - val_balanced_accuracy: 0.7283 - val_specificity: 0.9967 - val_miss_rate: 0.5400 - val_fall_out: 0.0033 - val_mcc: 0.6346
Epoch 73/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1357 - accuracy: 0.6033 - recall: 0.4105 - precision: 0.7610 - AUROC: 0.9303 - AUPRC: 0.6729 - f1_score: 0.5333 - balanced_accuracy: 0.6981 - specificity: 0.9857 - miss_rate: 0.5895 - fall_out: 0.0143 - mcc: 0.5261 - val_loss: 0.9766 - val_accuracy: 0.7150 - val_recall: 0.4550 - val_precision: 0.9100 - val_AUROC: 0.9509 - val_AUPRC: 0.7669 - val_f1_score: 0.6067 - val_balanced_accuracy: 0.7250 - val_specificity: 0.9950 - val_miss_rate: 0.5450 - val_fall_out: 0.0050 - val_mcc: 0.6194
Epoch 74/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1127 - accuracy: 0.6183 - recall: 0.4180 - precision: 0.7786 - AUROC: 0.9320 - AUPRC: 0.6835 - f1_score: 0.5440 - balanced_accuracy: 0.7024 - specificity: 0.9868 - miss_rate: 0.5820 - fall_out: 0.0132 - mcc: 0.5388 - val_loss: 0.9648 - val_accuracy: 0.7100 - val_recall: 0.4600 - val_precision: 0.9109 - val_AUROC: 0.9520 - val_AUPRC: 0.7708 - val_f1_score: 0.6113 - val_balanced_accuracy: 0.7275 - val_specificity: 0.9950 - val_miss_rate: 0.5400 - val_fall_out: 0.0050 - val_mcc: 0.6234
Epoch 75/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1203 - accuracy: 0.6020 - recall: 0.4230 - precision: 0.7545 - AUROC: 0.9327 - AUPRC: 0.6827 - f1_score: 0.5421 - balanced_accuracy: 0.7039 - specificity: 0.9847 - miss_rate: 0.5770 - fall_out: 0.0153 - mcc: 0.5317 - val_loss: 0.9574 - val_accuracy: 0.6850 - val_recall: 0.4750 - val_precision: 0.8962 - val_AUROC: 0.9527 - val_AUPRC: 0.7710 - val_f1_score: 0.6209 - val_balanced_accuracy: 0.7344 - val_specificity: 0.9939 - val_miss_rate: 0.5250 - val_fall_out: 0.0061 - val_mcc: 0.6279
Epoch 76/100
7/7 [==============================] - 0s 11ms/step - loss: 1.0377 - accuracy: 0.6308 - recall: 0.4355 - precision: 0.8188 - AUROC: 0.9432 - AUPRC: 0.7117 - f1_score: 0.5686 - balanced_accuracy: 0.7124 - specificity: 0.9893 - miss_rate: 0.5645 - fall_out: 0.0107 - mcc: 0.5679 - val_loss: 0.9421 - val_accuracy: 0.7000 - val_recall: 0.4900 - val_precision: 0.9074 - val_AUROC: 0.9541 - val_AUPRC: 0.7748 - val_f1_score: 0.6364 - val_balanced_accuracy: 0.7422 - val_specificity: 0.9944 - val_miss_rate: 0.5100 - val_fall_out: 0.0056 - val_mcc: 0.6430
Epoch 77/100
7/7 [==============================] - 0s 13ms/step - loss: 1.1048 - accuracy: 0.6345 - recall: 0.4268 - precision: 0.7595 - AUROC: 0.9353 - AUPRC: 0.6896 - f1_score: 0.5465 - balanced_accuracy: 0.7059 - specificity: 0.9850 - miss_rate: 0.5732 - fall_out: 0.0150 - mcc: 0.5364 - val_loss: 0.9329 - val_accuracy: 0.6950 - val_recall: 0.5050 - val_precision: 0.9099 - val_AUROC: 0.9547 - val_AUPRC: 0.7791 - val_f1_score: 0.6495 - val_balanced_accuracy: 0.7497 - val_specificity: 0.9944 - val_miss_rate: 0.4950 - val_fall_out: 0.0056 - val_mcc: 0.6544
Epoch 78/100
7/7 [==============================] - 0s 14ms/step - loss: 1.0298 - accuracy: 0.6496 - recall: 0.4656 - precision: 0.8000 - AUROC: 0.9428 - AUPRC: 0.7180 - f1_score: 0.5886 - balanced_accuracy: 0.7263 - specificity: 0.9871 - miss_rate: 0.5344 - fall_out: 0.0129 - mcc: 0.5800 - val_loss: 0.9224 - val_accuracy: 0.7050 - val_recall: 0.5200 - val_precision: 0.9204 - val_AUROC: 0.9552 - val_AUPRC: 0.7829 - val_f1_score: 0.6645 - val_balanced_accuracy: 0.7575 - val_specificity: 0.9950 - val_miss_rate: 0.4800 - val_fall_out: 0.0050 - val_mcc: 0.6692
Epoch 79/100
7/7 [==============================] - 0s 12ms/step - loss: 1.0525 - accuracy: 0.6383 - recall: 0.4743 - precision: 0.7979 - AUROC: 0.9385 - AUPRC: 0.7080 - f1_score: 0.5950 - balanced_accuracy: 0.7305 - specificity: 0.9866 - miss_rate: 0.5257 - fall_out: 0.0134 - mcc: 0.5849 - val_loss: 0.9210 - val_accuracy: 0.6950 - val_recall: 0.5200 - val_precision: 0.9043 - val_AUROC: 0.9554 - val_AUPRC: 0.7817 - val_f1_score: 0.6603 - val_balanced_accuracy: 0.7569 - val_specificity: 0.9939 - val_miss_rate: 0.4800 - val_fall_out: 0.0061 - val_mcc: 0.6622
Epoch 80/100
7/7 [==============================] - 0s 12ms/step - loss: 1.0923 - accuracy: 0.6195 - recall: 0.4268 - precision: 0.7663 - AUROC: 0.9333 - AUPRC: 0.6894 - f1_score: 0.5482 - balanced_accuracy: 0.7062 - specificity: 0.9855 - miss_rate: 0.5732 - fall_out: 0.0145 - mcc: 0.5394 - val_loss: 0.9202 - val_accuracy: 0.7050 - val_recall: 0.5050 - val_precision: 0.9018 - val_AUROC: 0.9552 - val_AUPRC: 0.7833 - val_f1_score: 0.6474 - val_balanced_accuracy: 0.7494 - val_specificity: 0.9939 - val_miss_rate: 0.4950 - val_fall_out: 0.0061 - val_mcc: 0.6509
Epoch 81/100
7/7 [==============================] - 0s 12ms/step - loss: 1.0417 - accuracy: 0.6496 - recall: 0.4543 - precision: 0.7874 - AUROC: 0.9404 - AUPRC: 0.7137 - f1_score: 0.5762 - balanced_accuracy: 0.7203 - specificity: 0.9864 - miss_rate: 0.5457 - fall_out: 0.0136 - mcc: 0.5670 - val_loss: 0.9094 - val_accuracy: 0.7150 - val_recall: 0.5150 - val_precision: 0.9115 - val_AUROC: 0.9557 - val_AUPRC: 0.7881 - val_f1_score: 0.6581 - val_balanced_accuracy: 0.7547 - val_specificity: 0.9944 - val_miss_rate: 0.4850 - val_fall_out: 0.0056 - val_mcc: 0.6619
Epoch 82/100
7/7 [==============================] - 0s 12ms/step - loss: 1.0484 - accuracy: 0.6383 - recall: 0.4581 - precision: 0.7738 - AUROC: 0.9381 - AUPRC: 0.7093 - f1_score: 0.5755 - balanced_accuracy: 0.7216 - specificity: 0.9851 - miss_rate: 0.5419 - fall_out: 0.0149 - mcc: 0.5634 - val_loss: 0.9062 - val_accuracy: 0.7150 - val_recall: 0.5350 - val_precision: 0.9068 - val_AUROC: 0.9561 - val_AUPRC: 0.7896 - val_f1_score: 0.6730 - val_balanced_accuracy: 0.7644 - val_specificity: 0.9939 - val_miss_rate: 0.4650 - val_fall_out: 0.0061 - val_mcc: 0.6734
Epoch 83/100
7/7 [==============================] - 0s 12ms/step - loss: 1.0031 - accuracy: 0.6496 - recall: 0.4919 - precision: 0.7988 - AUROC: 0.9442 - AUPRC: 0.7310 - f1_score: 0.6088 - balanced_accuracy: 0.7390 - specificity: 0.9862 - miss_rate: 0.5081 - fall_out: 0.0138 - mcc: 0.5967 - val_loss: 0.8902 - val_accuracy: 0.7150 - val_recall: 0.5400 - val_precision: 0.9153 - val_AUROC: 0.9573 - val_AUPRC: 0.7954 - val_f1_score: 0.6792 - val_balanced_accuracy: 0.7672 - val_specificity: 0.9944 - val_miss_rate: 0.4600 - val_fall_out: 0.0056 - val_mcc: 0.6805
Epoch 84/100
7/7 [==============================] - 0s 12ms/step - loss: 0.9958 - accuracy: 0.6533 - recall: 0.4831 - precision: 0.7814 - AUROC: 0.9458 - AUPRC: 0.7269 - f1_score: 0.5971 - balanced_accuracy: 0.7340 - specificity: 0.9850 - miss_rate: 0.5169 - fall_out: 0.0150 - mcc: 0.5831 - val_loss: 0.8761 - val_accuracy: 0.7300 - val_recall: 0.5350 - val_precision: 0.9068 - val_AUROC: 0.9587 - val_AUPRC: 0.8009 - val_f1_score: 0.6730 - val_balanced_accuracy: 0.7644 - val_specificity: 0.9939 - val_miss_rate: 0.4650 - val_fall_out: 0.0061 - val_mcc: 0.6734
Epoch 85/100
7/7 [==============================] - 0s 13ms/step - loss: 0.9859 - accuracy: 0.6471 - recall: 0.4844 - precision: 0.8062 - AUROC: 0.9459 - AUPRC: 0.7381 - f1_score: 0.6052 - balanced_accuracy: 0.7357 - specificity: 0.9871 - miss_rate: 0.5156 - fall_out: 0.0129 - mcc: 0.5952 - val_loss: 0.8665 - val_accuracy: 0.7150 - val_recall: 0.5450 - val_precision: 0.9083 - val_AUROC: 0.9594 - val_AUPRC: 0.8036 - val_f1_score: 0.6812 - val_balanced_accuracy: 0.7694 - val_specificity: 0.9939 - val_miss_rate: 0.4550 - val_fall_out: 0.0061 - val_mcc: 0.6807
Epoch 86/100
7/7 [==============================] - 0s 12ms/step - loss: 1.0033 - accuracy: 0.6696 - recall: 0.4956 - precision: 0.7873 - AUROC: 0.9450 - AUPRC: 0.7254 - f1_score: 0.6083 - balanced_accuracy: 0.7404 - specificity: 0.9851 - miss_rate: 0.5044 - fall_out: 0.0149 - mcc: 0.5938 - val_loss: 0.8666 - val_accuracy: 0.7100 - val_recall: 0.5600 - val_precision: 0.9256 - val_AUROC: 0.9596 - val_AUPRC: 0.8043 - val_f1_score: 0.6978 - val_balanced_accuracy: 0.7775 - val_specificity: 0.9950 - val_miss_rate: 0.4400 - val_fall_out: 0.0050 - val_mcc: 0.6984
Epoch 87/100
7/7 [==============================] - 0s 12ms/step - loss: 0.9944 - accuracy: 0.6521 - recall: 0.4944 - precision: 0.8028 - AUROC: 0.9455 - AUPRC: 0.7346 - f1_score: 0.6119 - balanced_accuracy: 0.7404 - specificity: 0.9865 - miss_rate: 0.5056 - fall_out: 0.0135 - mcc: 0.6001 - val_loss: 0.8668 - val_accuracy: 0.7150 - val_recall: 0.5400 - val_precision: 0.9000 - val_AUROC: 0.9594 - val_AUPRC: 0.8039 - val_f1_score: 0.6750 - val_balanced_accuracy: 0.7667 - val_specificity: 0.9933 - val_miss_rate: 0.4600 - val_fall_out: 0.0067 - val_mcc: 0.6737
25/25 [==============================] - 0s 5ms/step - loss: 0.6460 - accuracy: 0.8135 - recall: 0.6320 - precision: 0.9165 - AUROC: 0.9818 - AUPRC: 0.8912 - f1_score: 0.7481 - balanced_accuracy: 0.8128 - specificity: 0.9936 - miss_rate: 0.3680 - fall_out: 0.0064 - mcc: 0.7407
7/7 [==============================] - 0s 5ms/step - loss: 0.8668 - accuracy: 0.7150 - recall: 0.5400 - precision: 0.9000 - AUROC: 0.9594 - AUPRC: 0.8039 - f1_score: 0.6750 - balanced_accuracy: 0.7667 - specificity: 0.9933 - miss_rate: 0.4600 - fall_out: 0.0067 - mcc: 0.6737
4it [00:35, 8.95s/it]
-- HOLDOUT 5 -- WINDOW window_30s
-- 24 Uncorrelated features: [Pearson+Spearman]
['mfcc16_mean', 'harmony_mean', 'mfcc10_var', 'mfcc4_mean', 'mfcc15_mean', 'mfcc19_mean', 'mfcc6_mean', 'mfcc8_var', 'mfcc9_var', 'mfcc20_mean', 'mfcc3_mean', 'chroma_stft_var', 'mfcc3_var', 'mfcc1_var', 'mfcc2_var', 'mfcc7_var', 'tempo', 'mfcc13_mean', 'mfcc5_mean', 'mfcc17_mean', 'mfcc14_mean', 'perceptr_mean', 'mfcc5_var', 'rms_var']
-- Actually uncorrelated features: [confirmed via MIC]
[]
-- 1 Correlated features: [Pearson]
['spectral_centroid_mean']
Model: "MLP"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_196 (Dense) (None, 128) 7296
dropout_152 (Dropout) (None, 128) 0
dense_197 (Dense) (None, 64) 8256
dropout_153 (Dropout) (None, 64) 0
dense_198 (Dense) (None, 64) 4160
dropout_154 (Dropout) (None, 64) 0
dense_199 (Dense) (None, 10) 650
=================================================================
Total params: 20,362
Trainable params: 20,362
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
7/7 [==============================] - 2s 84ms/step - loss: 2.8944 - accuracy: 0.0951 - recall: 0.0088 - precision: 0.1061 - AUROC: 0.4919 - AUPRC: 0.0970 - f1_score: 0.0162 - balanced_accuracy: 0.5003 - specificity: 0.9918 - miss_rate: 0.9912 - fall_out: 0.0082 - mcc: 0.0018 - val_loss: 2.2850 - val_accuracy: 0.1100 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5683 - val_AUPRC: 0.1178 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 2/100
7/7 [==============================] - 0s 14ms/step - loss: 2.6123 - accuracy: 0.1252 - recall: 0.0113 - precision: 0.1698 - AUROC: 0.5345 - AUPRC: 0.1131 - f1_score: 0.0211 - balanced_accuracy: 0.5026 - specificity: 0.9939 - miss_rate: 0.9887 - fall_out: 0.0061 - mcc: 0.0190 - val_loss: 2.2417 - val_accuracy: 0.1850 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6285 - val_AUPRC: 0.1542 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 3/100
7/7 [==============================] - 0s 15ms/step - loss: 2.5584 - accuracy: 0.1414 - recall: 0.0025 - precision: 0.0526 - AUROC: 0.5555 - AUPRC: 0.1182 - f1_score: 0.0048 - balanced_accuracy: 0.4987 - specificity: 0.9950 - miss_rate: 0.9975 - fall_out: 0.0050 - mcc: -0.0109 - val_loss: 2.2098 - val_accuracy: 0.2400 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6746 - val_AUPRC: 0.2041 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 4/100
7/7 [==============================] - 0s 16ms/step - loss: 2.4796 - accuracy: 0.1389 - recall: 0.0100 - precision: 0.2000 - AUROC: 0.5714 - AUPRC: 0.1282 - f1_score: 0.0191 - balanced_accuracy: 0.5028 - specificity: 0.9955 - miss_rate: 0.9900 - fall_out: 0.0045 - mcc: 0.0236 - val_loss: 2.1821 - val_accuracy: 0.2750 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7084 - val_AUPRC: 0.2410 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 5/100
7/7 [==============================] - 0s 14ms/step - loss: 2.3431 - accuracy: 0.1690 - recall: 0.0113 - precision: 0.3103 - AUROC: 0.6027 - AUPRC: 0.1479 - f1_score: 0.0217 - balanced_accuracy: 0.5042 - specificity: 0.9972 - miss_rate: 0.9887 - fall_out: 0.0028 - mcc: 0.0423 - val_loss: 2.1555 - val_accuracy: 0.3000 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7295 - val_AUPRC: 0.2741 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 6/100
7/7 [==============================] - 0s 14ms/step - loss: 2.4019 - accuracy: 0.1765 - recall: 0.0088 - precision: 0.2188 - AUROC: 0.6290 - AUPRC: 0.1551 - f1_score: 0.0168 - balanced_accuracy: 0.5026 - specificity: 0.9965 - miss_rate: 0.9912 - fall_out: 0.0035 - mcc: 0.0251 - val_loss: 2.1180 - val_accuracy: 0.3500 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7538 - val_AUPRC: 0.3106 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 7/100
7/7 [==============================] - 0s 13ms/step - loss: 2.3476 - accuracy: 0.2128 - recall: 0.0100 - precision: 0.2162 - AUROC: 0.6393 - AUPRC: 0.1665 - f1_score: 0.0191 - balanced_accuracy: 0.5030 - specificity: 0.9960 - miss_rate: 0.9900 - fall_out: 0.0040 - mcc: 0.0264 - val_loss: 2.0829 - val_accuracy: 0.3600 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7683 - val_AUPRC: 0.3374 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 8/100
7/7 [==============================] - 0s 13ms/step - loss: 2.2605 - accuracy: 0.2140 - recall: 0.0138 - precision: 0.4074 - AUROC: 0.6720 - AUPRC: 0.1904 - f1_score: 0.0266 - balanced_accuracy: 0.5058 - specificity: 0.9978 - miss_rate: 0.9862 - fall_out: 0.0022 - mcc: 0.0597 - val_loss: 2.0445 - val_accuracy: 0.3800 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7754 - val_AUPRC: 0.3501 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 9/100
7/7 [==============================] - 0s 12ms/step - loss: 2.2237 - accuracy: 0.2353 - recall: 0.0225 - precision: 0.4500 - AUROC: 0.6920 - AUPRC: 0.2141 - f1_score: 0.0429 - balanced_accuracy: 0.5097 - specificity: 0.9969 - miss_rate: 0.9775 - fall_out: 0.0031 - mcc: 0.0828 - val_loss: 2.0056 - val_accuracy: 0.3900 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7805 - val_AUPRC: 0.3603 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 10/100
7/7 [==============================] - 0s 12ms/step - loss: 2.2175 - accuracy: 0.2416 - recall: 0.0225 - precision: 0.3750 - AUROC: 0.6906 - AUPRC: 0.2117 - f1_score: 0.0425 - balanced_accuracy: 0.5092 - specificity: 0.9958 - miss_rate: 0.9775 - fall_out: 0.0042 - mcc: 0.0713 - val_loss: 1.9640 - val_accuracy: 0.4000 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7937 - val_AUPRC: 0.3845 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 11/100
7/7 [==============================] - 0s 12ms/step - loss: 2.0617 - accuracy: 0.2666 - recall: 0.0325 - precision: 0.5098 - AUROC: 0.7264 - AUPRC: 0.2525 - f1_score: 0.0612 - balanced_accuracy: 0.5145 - specificity: 0.9965 - miss_rate: 0.9675 - fall_out: 0.0035 - mcc: 0.1095 - val_loss: 1.9118 - val_accuracy: 0.4050 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.8153 - val_AUPRC: 0.4138 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 12/100
7/7 [==============================] - 0s 12ms/step - loss: 2.1132 - accuracy: 0.2591 - recall: 0.0388 - precision: 0.4844 - AUROC: 0.7307 - AUPRC: 0.2437 - f1_score: 0.0718 - balanced_accuracy: 0.5171 - specificity: 0.9954 - miss_rate: 0.9612 - fall_out: 0.0046 - mcc: 0.1151 - val_loss: 1.8719 - val_accuracy: 0.4100 - val_recall: 0.0250 - val_precision: 0.8333 - val_AUROC: 0.8227 - val_AUPRC: 0.4236 - val_f1_score: 0.0485 - val_balanced_accuracy: 0.5122 - val_specificity: 0.9994 - val_miss_rate: 0.9750 - val_fall_out: 5.5556e-04 - val_mcc: 0.1341
Epoch 13/100
7/7 [==============================] - 0s 11ms/step - loss: 2.0288 - accuracy: 0.2804 - recall: 0.0626 - precision: 0.6173 - AUROC: 0.7487 - AUPRC: 0.2885 - f1_score: 0.1136 - balanced_accuracy: 0.5291 - specificity: 0.9957 - miss_rate: 0.9374 - fall_out: 0.0043 - mcc: 0.1745 - val_loss: 1.8345 - val_accuracy: 0.4200 - val_recall: 0.0650 - val_precision: 0.8667 - val_AUROC: 0.8307 - val_AUPRC: 0.4323 - val_f1_score: 0.1209 - val_balanced_accuracy: 0.5319 - val_specificity: 0.9989 - val_miss_rate: 0.9350 - val_fall_out: 0.0011 - val_mcc: 0.2222
Epoch 14/100
7/7 [==============================] - 0s 12ms/step - loss: 2.0357 - accuracy: 0.3041 - recall: 0.0726 - precision: 0.6170 - AUROC: 0.7474 - AUPRC: 0.2932 - f1_score: 0.1299 - balanced_accuracy: 0.5338 - specificity: 0.9950 - miss_rate: 0.9274 - fall_out: 0.0050 - mcc: 0.1880 - val_loss: 1.7949 - val_accuracy: 0.4300 - val_recall: 0.0850 - val_precision: 0.8947 - val_AUROC: 0.8402 - val_AUPRC: 0.4383 - val_f1_score: 0.1553 - val_balanced_accuracy: 0.5419 - val_specificity: 0.9989 - val_miss_rate: 0.9150 - val_fall_out: 0.0011 - val_mcc: 0.2594
Epoch 15/100
7/7 [==============================] - 0s 11ms/step - loss: 1.9443 - accuracy: 0.3154 - recall: 0.0751 - precision: 0.5505 - AUROC: 0.7809 - AUPRC: 0.3172 - f1_score: 0.1322 - balanced_accuracy: 0.5341 - specificity: 0.9932 - miss_rate: 0.9249 - fall_out: 0.0068 - mcc: 0.1766 - val_loss: 1.7578 - val_accuracy: 0.4650 - val_recall: 0.0950 - val_precision: 0.8636 - val_AUROC: 0.8451 - val_AUPRC: 0.4564 - val_f1_score: 0.1712 - val_balanced_accuracy: 0.5467 - val_specificity: 0.9983 - val_miss_rate: 0.9050 - val_fall_out: 0.0017 - val_mcc: 0.2685
Epoch 16/100
7/7 [==============================] - 0s 12ms/step - loss: 1.9595 - accuracy: 0.3104 - recall: 0.0901 - precision: 0.5373 - AUROC: 0.7727 - AUPRC: 0.2986 - f1_score: 0.1543 - balanced_accuracy: 0.5407 - specificity: 0.9914 - miss_rate: 0.9099 - fall_out: 0.0086 - mcc: 0.1904 - val_loss: 1.7320 - val_accuracy: 0.4750 - val_recall: 0.1000 - val_precision: 0.9524 - val_AUROC: 0.8509 - val_AUPRC: 0.4677 - val_f1_score: 0.1810 - val_balanced_accuracy: 0.5497 - val_specificity: 0.9994 - val_miss_rate: 0.9000 - val_fall_out: 5.5556e-04 - val_mcc: 0.2927
Epoch 17/100
7/7 [==============================] - 0s 12ms/step - loss: 1.9132 - accuracy: 0.3204 - recall: 0.0826 - precision: 0.5500 - AUROC: 0.7831 - AUPRC: 0.3229 - f1_score: 0.1436 - balanced_accuracy: 0.5375 - specificity: 0.9925 - miss_rate: 0.9174 - fall_out: 0.0075 - mcc: 0.1852 - val_loss: 1.6956 - val_accuracy: 0.4650 - val_recall: 0.1150 - val_precision: 0.8519 - val_AUROC: 0.8552 - val_AUPRC: 0.4778 - val_f1_score: 0.2026 - val_balanced_accuracy: 0.5564 - val_specificity: 0.9978 - val_miss_rate: 0.8850 - val_fall_out: 0.0022 - val_mcc: 0.2932
Epoch 18/100
7/7 [==============================] - 0s 12ms/step - loss: 1.8696 - accuracy: 0.3417 - recall: 0.0964 - precision: 0.5833 - AUROC: 0.7994 - AUPRC: 0.3417 - f1_score: 0.1654 - balanced_accuracy: 0.5444 - specificity: 0.9924 - miss_rate: 0.9036 - fall_out: 0.0076 - mcc: 0.2088 - val_loss: 1.6715 - val_accuracy: 0.4600 - val_recall: 0.1300 - val_precision: 0.8966 - val_AUROC: 0.8576 - val_AUPRC: 0.4867 - val_f1_score: 0.2271 - val_balanced_accuracy: 0.5642 - val_specificity: 0.9983 - val_miss_rate: 0.8700 - val_fall_out: 0.0017 - val_mcc: 0.3221
Epoch 19/100
7/7 [==============================] - 0s 13ms/step - loss: 1.8554 - accuracy: 0.3667 - recall: 0.1227 - precision: 0.6712 - AUROC: 0.7993 - AUPRC: 0.3611 - f1_score: 0.2074 - balanced_accuracy: 0.5580 - specificity: 0.9933 - miss_rate: 0.8773 - fall_out: 0.0067 - mcc: 0.2598 - val_loss: 1.6443 - val_accuracy: 0.4650 - val_recall: 0.1450 - val_precision: 0.8056 - val_AUROC: 0.8632 - val_AUPRC: 0.4922 - val_f1_score: 0.2458 - val_balanced_accuracy: 0.5706 - val_specificity: 0.9961 - val_miss_rate: 0.8550 - val_fall_out: 0.0039 - val_mcc: 0.3184
Epoch 20/100
7/7 [==============================] - 0s 12ms/step - loss: 1.8494 - accuracy: 0.3630 - recall: 0.1139 - precision: 0.5759 - AUROC: 0.8020 - AUPRC: 0.3500 - f1_score: 0.1902 - balanced_accuracy: 0.5523 - specificity: 0.9907 - miss_rate: 0.8861 - fall_out: 0.0093 - mcc: 0.2253 - val_loss: 1.6242 - val_accuracy: 0.4700 - val_recall: 0.1500 - val_precision: 0.8108 - val_AUROC: 0.8679 - val_AUPRC: 0.5017 - val_f1_score: 0.2532 - val_balanced_accuracy: 0.5731 - val_specificity: 0.9961 - val_miss_rate: 0.8500 - val_fall_out: 0.0039 - val_mcc: 0.3253
Epoch 21/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7730 - accuracy: 0.3830 - recall: 0.1339 - precision: 0.6294 - AUROC: 0.8188 - AUPRC: 0.3986 - f1_score: 0.2208 - balanced_accuracy: 0.5626 - specificity: 0.9912 - miss_rate: 0.8661 - fall_out: 0.0088 - mcc: 0.2602 - val_loss: 1.6088 - val_accuracy: 0.4750 - val_recall: 0.1600 - val_precision: 0.8205 - val_AUROC: 0.8711 - val_AUPRC: 0.5054 - val_f1_score: 0.2678 - val_balanced_accuracy: 0.5781 - val_specificity: 0.9961 - val_miss_rate: 0.8400 - val_fall_out: 0.0039 - val_mcc: 0.3387
Epoch 22/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7876 - accuracy: 0.3517 - recall: 0.1277 - precision: 0.6071 - AUROC: 0.8214 - AUPRC: 0.3706 - f1_score: 0.2110 - balanced_accuracy: 0.5592 - specificity: 0.9908 - miss_rate: 0.8723 - fall_out: 0.0092 - mcc: 0.2477 - val_loss: 1.5895 - val_accuracy: 0.4950 - val_recall: 0.1600 - val_precision: 0.8000 - val_AUROC: 0.8752 - val_AUPRC: 0.5141 - val_f1_score: 0.2667 - val_balanced_accuracy: 0.5778 - val_specificity: 0.9956 - val_miss_rate: 0.8400 - val_fall_out: 0.0044 - val_mcc: 0.3333
Epoch 23/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7896 - accuracy: 0.3792 - recall: 0.1539 - precision: 0.6613 - AUROC: 0.8157 - AUPRC: 0.3828 - f1_score: 0.2497 - balanced_accuracy: 0.5726 - specificity: 0.9912 - miss_rate: 0.8461 - fall_out: 0.0088 - mcc: 0.2888 - val_loss: 1.5749 - val_accuracy: 0.4900 - val_recall: 0.1600 - val_precision: 0.8000 - val_AUROC: 0.8781 - val_AUPRC: 0.5195 - val_f1_score: 0.2667 - val_balanced_accuracy: 0.5778 - val_specificity: 0.9956 - val_miss_rate: 0.8400 - val_fall_out: 0.0044 - val_mcc: 0.3333
Epoch 24/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7396 - accuracy: 0.3930 - recall: 0.1552 - precision: 0.6359 - AUROC: 0.8288 - AUPRC: 0.4018 - f1_score: 0.2495 - balanced_accuracy: 0.5727 - specificity: 0.9901 - miss_rate: 0.8448 - fall_out: 0.0099 - mcc: 0.2825 - val_loss: 1.5555 - val_accuracy: 0.4900 - val_recall: 0.1750 - val_precision: 0.8140 - val_AUROC: 0.8808 - val_AUPRC: 0.5285 - val_f1_score: 0.2881 - val_balanced_accuracy: 0.5853 - val_specificity: 0.9956 - val_miss_rate: 0.8250 - val_fall_out: 0.0044 - val_mcc: 0.3528
Epoch 25/100
7/7 [==============================] - 0s 11ms/step - loss: 1.7418 - accuracy: 0.3655 - recall: 0.1314 - precision: 0.6105 - AUROC: 0.8295 - AUPRC: 0.3875 - f1_score: 0.2163 - balanced_accuracy: 0.5610 - specificity: 0.9907 - miss_rate: 0.8686 - fall_out: 0.0093 - mcc: 0.2524 - val_loss: 1.5335 - val_accuracy: 0.5100 - val_recall: 0.1850 - val_precision: 0.8222 - val_AUROC: 0.8845 - val_AUPRC: 0.5367 - val_f1_score: 0.3020 - val_balanced_accuracy: 0.5903 - val_specificity: 0.9956 - val_miss_rate: 0.8150 - val_fall_out: 0.0044 - val_mcc: 0.3652
Epoch 26/100
7/7 [==============================] - 0s 11ms/step - loss: 1.7081 - accuracy: 0.3705 - recall: 0.1502 - precision: 0.6417 - AUROC: 0.8330 - AUPRC: 0.4009 - f1_score: 0.2434 - balanced_accuracy: 0.5704 - specificity: 0.9907 - miss_rate: 0.8498 - fall_out: 0.0093 - mcc: 0.2795 - val_loss: 1.5154 - val_accuracy: 0.5300 - val_recall: 0.2000 - val_precision: 0.8000 - val_AUROC: 0.8864 - val_AUPRC: 0.5426 - val_f1_score: 0.3200 - val_balanced_accuracy: 0.5972 - val_specificity: 0.9944 - val_miss_rate: 0.8000 - val_fall_out: 0.0056 - val_mcc: 0.3736
Epoch 27/100
7/7 [==============================] - 0s 12ms/step - loss: 1.6470 - accuracy: 0.4055 - recall: 0.1740 - precision: 0.6748 - AUROC: 0.8506 - AUPRC: 0.4327 - f1_score: 0.2766 - balanced_accuracy: 0.5823 - specificity: 0.9907 - miss_rate: 0.8260 - fall_out: 0.0093 - mcc: 0.3117 - val_loss: 1.4949 - val_accuracy: 0.5050 - val_recall: 0.2050 - val_precision: 0.8200 - val_AUROC: 0.8896 - val_AUPRC: 0.5483 - val_f1_score: 0.3280 - val_balanced_accuracy: 0.6000 - val_specificity: 0.9950 - val_miss_rate: 0.7950 - val_fall_out: 0.0050 - val_mcc: 0.3843
Epoch 28/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7004 - accuracy: 0.4143 - recall: 0.1677 - precision: 0.6291 - AUROC: 0.8410 - AUPRC: 0.4254 - f1_score: 0.2648 - balanced_accuracy: 0.5784 - specificity: 0.9890 - miss_rate: 0.8323 - fall_out: 0.0110 - mcc: 0.2919 - val_loss: 1.4797 - val_accuracy: 0.5150 - val_recall: 0.2100 - val_precision: 0.8077 - val_AUROC: 0.8938 - val_AUPRC: 0.5515 - val_f1_score: 0.3333 - val_balanced_accuracy: 0.6022 - val_specificity: 0.9944 - val_miss_rate: 0.7900 - val_fall_out: 0.0056 - val_mcc: 0.3854
Epoch 29/100
7/7 [==============================] - 0s 12ms/step - loss: 1.6787 - accuracy: 0.3955 - recall: 0.1715 - precision: 0.6062 - AUROC: 0.8437 - AUPRC: 0.4137 - f1_score: 0.2673 - balanced_accuracy: 0.5795 - specificity: 0.9876 - miss_rate: 0.8285 - fall_out: 0.0124 - mcc: 0.2879 - val_loss: 1.4595 - val_accuracy: 0.5100 - val_recall: 0.2050 - val_precision: 0.7885 - val_AUROC: 0.8979 - val_AUPRC: 0.5592 - val_f1_score: 0.3254 - val_balanced_accuracy: 0.5994 - val_specificity: 0.9939 - val_miss_rate: 0.7950 - val_fall_out: 0.0061 - val_mcc: 0.3749
Epoch 30/100
7/7 [==============================] - 0s 14ms/step - loss: 1.6451 - accuracy: 0.4143 - recall: 0.2003 - precision: 0.6324 - AUROC: 0.8528 - AUPRC: 0.4493 - f1_score: 0.3042 - balanced_accuracy: 0.5937 - specificity: 0.9871 - miss_rate: 0.7997 - fall_out: 0.0129 - mcc: 0.3209 - val_loss: 1.4391 - val_accuracy: 0.5300 - val_recall: 0.2050 - val_precision: 0.8039 - val_AUROC: 0.9002 - val_AUPRC: 0.5714 - val_f1_score: 0.3267 - val_balanced_accuracy: 0.5997 - val_specificity: 0.9944 - val_miss_rate: 0.7950 - val_fall_out: 0.0056 - val_mcc: 0.3796
Epoch 31/100
7/7 [==============================] - 0s 15ms/step - loss: 1.6486 - accuracy: 0.4093 - recall: 0.1977 - precision: 0.6245 - AUROC: 0.8483 - AUPRC: 0.4371 - f1_score: 0.3004 - balanced_accuracy: 0.5923 - specificity: 0.9868 - miss_rate: 0.8023 - fall_out: 0.0132 - mcc: 0.3162 - val_loss: 1.4234 - val_accuracy: 0.5300 - val_recall: 0.2100 - val_precision: 0.7925 - val_AUROC: 0.9030 - val_AUPRC: 0.5776 - val_f1_score: 0.3320 - val_balanced_accuracy: 0.6019 - val_specificity: 0.9939 - val_miss_rate: 0.7900 - val_fall_out: 0.0061 - val_mcc: 0.3808
Epoch 32/100
7/7 [==============================] - 0s 14ms/step - loss: 1.6047 - accuracy: 0.4330 - recall: 0.2078 - precision: 0.6561 - AUROC: 0.8564 - AUPRC: 0.4407 - f1_score: 0.3156 - balanced_accuracy: 0.5978 - specificity: 0.9879 - miss_rate: 0.7922 - fall_out: 0.0121 - mcc: 0.3352 - val_loss: 1.4090 - val_accuracy: 0.5200 - val_recall: 0.2200 - val_precision: 0.7719 - val_AUROC: 0.9043 - val_AUPRC: 0.5821 - val_f1_score: 0.3424 - val_balanced_accuracy: 0.6064 - val_specificity: 0.9928 - val_miss_rate: 0.7800 - val_fall_out: 0.0072 - val_mcc: 0.3836
Epoch 33/100
7/7 [==============================] - 0s 12ms/step - loss: 1.5484 - accuracy: 0.4506 - recall: 0.2028 - precision: 0.6532 - AUROC: 0.8656 - AUPRC: 0.4733 - f1_score: 0.3095 - balanced_accuracy: 0.5954 - specificity: 0.9880 - miss_rate: 0.7972 - fall_out: 0.0120 - mcc: 0.3301 - val_loss: 1.3958 - val_accuracy: 0.5250 - val_recall: 0.2400 - val_precision: 0.7869 - val_AUROC: 0.9055 - val_AUPRC: 0.5859 - val_f1_score: 0.3678 - val_balanced_accuracy: 0.6164 - val_specificity: 0.9928 - val_miss_rate: 0.7600 - val_fall_out: 0.0072 - val_mcc: 0.4061
Epoch 34/100
7/7 [==============================] - 0s 12ms/step - loss: 1.5723 - accuracy: 0.4531 - recall: 0.2303 - precision: 0.6765 - AUROC: 0.8629 - AUPRC: 0.4794 - f1_score: 0.3436 - balanced_accuracy: 0.6090 - specificity: 0.9878 - miss_rate: 0.7697 - fall_out: 0.0122 - mcc: 0.3607 - val_loss: 1.3842 - val_accuracy: 0.5250 - val_recall: 0.2550 - val_precision: 0.7612 - val_AUROC: 0.9072 - val_AUPRC: 0.5876 - val_f1_score: 0.3820 - val_balanced_accuracy: 0.6231 - val_specificity: 0.9911 - val_miss_rate: 0.7450 - val_fall_out: 0.0089 - val_mcc: 0.4103
Epoch 35/100
7/7 [==============================] - 0s 13ms/step - loss: 1.5432 - accuracy: 0.4618 - recall: 0.2190 - precision: 0.6731 - AUROC: 0.8747 - AUPRC: 0.4813 - f1_score: 0.3305 - balanced_accuracy: 0.6036 - specificity: 0.9882 - miss_rate: 0.7810 - fall_out: 0.0118 - mcc: 0.3503 - val_loss: 1.3657 - val_accuracy: 0.5300 - val_recall: 0.2450 - val_precision: 0.7538 - val_AUROC: 0.9089 - val_AUPRC: 0.5952 - val_f1_score: 0.3698 - val_balanced_accuracy: 0.6181 - val_specificity: 0.9911 - val_miss_rate: 0.7550 - val_fall_out: 0.0089 - val_mcc: 0.3995
Epoch 36/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4702 - accuracy: 0.4681 - recall: 0.2478 - precision: 0.6899 - AUROC: 0.8790 - AUPRC: 0.5139 - f1_score: 0.3646 - balanced_accuracy: 0.6177 - specificity: 0.9876 - miss_rate: 0.7522 - fall_out: 0.0124 - mcc: 0.3795 - val_loss: 1.3492 - val_accuracy: 0.5200 - val_recall: 0.2650 - val_precision: 0.7465 - val_AUROC: 0.9105 - val_AUPRC: 0.6007 - val_f1_score: 0.3911 - val_balanced_accuracy: 0.6275 - val_specificity: 0.9900 - val_miss_rate: 0.7350 - val_fall_out: 0.0100 - val_mcc: 0.4134
Epoch 37/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4499 - accuracy: 0.4806 - recall: 0.2516 - precision: 0.6907 - AUROC: 0.8820 - AUPRC: 0.5142 - f1_score: 0.3688 - balanced_accuracy: 0.6195 - specificity: 0.9875 - miss_rate: 0.7484 - fall_out: 0.0125 - mcc: 0.3828 - val_loss: 1.3374 - val_accuracy: 0.5150 - val_recall: 0.2750 - val_precision: 0.7534 - val_AUROC: 0.9120 - val_AUPRC: 0.6056 - val_f1_score: 0.4029 - val_balanced_accuracy: 0.6325 - val_specificity: 0.9900 - val_miss_rate: 0.7250 - val_fall_out: 0.0100 - val_mcc: 0.4239
Epoch 38/100
7/7 [==============================] - 0s 12ms/step - loss: 1.5193 - accuracy: 0.4806 - recall: 0.2441 - precision: 0.6842 - AUROC: 0.8726 - AUPRC: 0.4952 - f1_score: 0.3598 - balanced_accuracy: 0.6158 - specificity: 0.9875 - miss_rate: 0.7559 - fall_out: 0.0125 - mcc: 0.3745 - val_loss: 1.3332 - val_accuracy: 0.5350 - val_recall: 0.2750 - val_precision: 0.7432 - val_AUROC: 0.9125 - val_AUPRC: 0.6042 - val_f1_score: 0.4015 - val_balanced_accuracy: 0.6322 - val_specificity: 0.9894 - val_miss_rate: 0.7250 - val_fall_out: 0.0106 - val_mcc: 0.4203
Epoch 39/100
7/7 [==============================] - 0s 12ms/step - loss: 1.5196 - accuracy: 0.4743 - recall: 0.2503 - precision: 0.6579 - AUROC: 0.8715 - AUPRC: 0.4899 - f1_score: 0.3626 - balanced_accuracy: 0.6179 - specificity: 0.9855 - miss_rate: 0.7497 - fall_out: 0.0145 - mcc: 0.3698 - val_loss: 1.3177 - val_accuracy: 0.5300 - val_recall: 0.2850 - val_precision: 0.7808 - val_AUROC: 0.9142 - val_AUPRC: 0.6107 - val_f1_score: 0.4176 - val_balanced_accuracy: 0.6381 - val_specificity: 0.9911 - val_miss_rate: 0.7150 - val_fall_out: 0.0089 - val_mcc: 0.4417
Epoch 40/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4969 - accuracy: 0.4718 - recall: 0.2641 - precision: 0.6895 - AUROC: 0.8775 - AUPRC: 0.5101 - f1_score: 0.3819 - balanced_accuracy: 0.6254 - specificity: 0.9868 - miss_rate: 0.7359 - fall_out: 0.0132 - mcc: 0.3922 - val_loss: 1.3090 - val_accuracy: 0.5450 - val_recall: 0.2800 - val_precision: 0.8000 - val_AUROC: 0.9156 - val_AUPRC: 0.6181 - val_f1_score: 0.4148 - val_balanced_accuracy: 0.6361 - val_specificity: 0.9922 - val_miss_rate: 0.7200 - val_fall_out: 0.0078 - val_mcc: 0.4444
Epoch 41/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4268 - accuracy: 0.4981 - recall: 0.2816 - precision: 0.6902 - AUROC: 0.8865 - AUPRC: 0.5294 - f1_score: 0.4000 - balanced_accuracy: 0.6338 - specificity: 0.9860 - miss_rate: 0.7184 - fall_out: 0.0140 - mcc: 0.4057 - val_loss: 1.3001 - val_accuracy: 0.5400 - val_recall: 0.2800 - val_precision: 0.7887 - val_AUROC: 0.9174 - val_AUPRC: 0.6218 - val_f1_score: 0.4133 - val_balanced_accuracy: 0.6358 - val_specificity: 0.9917 - val_miss_rate: 0.7200 - val_fall_out: 0.0083 - val_mcc: 0.4404
Epoch 42/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4531 - accuracy: 0.5019 - recall: 0.2678 - precision: 0.6751 - AUROC: 0.8819 - AUPRC: 0.5212 - f1_score: 0.3835 - balanced_accuracy: 0.6268 - specificity: 0.9857 - miss_rate: 0.7322 - fall_out: 0.0143 - mcc: 0.3896 - val_loss: 1.2985 - val_accuracy: 0.5300 - val_recall: 0.2850 - val_precision: 0.7917 - val_AUROC: 0.9169 - val_AUPRC: 0.6203 - val_f1_score: 0.4191 - val_balanced_accuracy: 0.6383 - val_specificity: 0.9917 - val_miss_rate: 0.7150 - val_fall_out: 0.0083 - val_mcc: 0.4455
Epoch 43/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4723 - accuracy: 0.5081 - recall: 0.2603 - precision: 0.6820 - AUROC: 0.8807 - AUPRC: 0.5208 - f1_score: 0.3768 - balanced_accuracy: 0.6234 - specificity: 0.9865 - miss_rate: 0.7397 - fall_out: 0.0135 - mcc: 0.3865 - val_loss: 1.2897 - val_accuracy: 0.5550 - val_recall: 0.2950 - val_precision: 0.8082 - val_AUROC: 0.9187 - val_AUPRC: 0.6350 - val_f1_score: 0.4322 - val_balanced_accuracy: 0.6436 - val_specificity: 0.9922 - val_miss_rate: 0.7050 - val_fall_out: 0.0078 - val_mcc: 0.4595
Epoch 44/100
7/7 [==============================] - 0s 12ms/step - loss: 1.5164 - accuracy: 0.4819 - recall: 0.2403 - precision: 0.6621 - AUROC: 0.8769 - AUPRC: 0.4946 - f1_score: 0.3526 - balanced_accuracy: 0.6133 - specificity: 0.9864 - miss_rate: 0.7597 - fall_out: 0.0136 - mcc: 0.3636 - val_loss: 1.2754 - val_accuracy: 0.5650 - val_recall: 0.3000 - val_precision: 0.8000 - val_AUROC: 0.9203 - val_AUPRC: 0.6435 - val_f1_score: 0.4364 - val_balanced_accuracy: 0.6458 - val_specificity: 0.9917 - val_miss_rate: 0.7000 - val_fall_out: 0.0083 - val_mcc: 0.4606
Epoch 45/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4460 - accuracy: 0.5069 - recall: 0.2716 - precision: 0.7023 - AUROC: 0.8839 - AUPRC: 0.5362 - f1_score: 0.3917 - balanced_accuracy: 0.6294 - specificity: 0.9872 - miss_rate: 0.7284 - fall_out: 0.0128 - mcc: 0.4027 - val_loss: 1.2690 - val_accuracy: 0.5700 - val_recall: 0.3100 - val_precision: 0.7949 - val_AUROC: 0.9205 - val_AUPRC: 0.6452 - val_f1_score: 0.4460 - val_balanced_accuracy: 0.6506 - val_specificity: 0.9911 - val_miss_rate: 0.6900 - val_fall_out: 0.0089 - val_mcc: 0.4666
Epoch 46/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4415 - accuracy: 0.5156 - recall: 0.2804 - precision: 0.7089 - AUROC: 0.8898 - AUPRC: 0.5352 - f1_score: 0.4018 - balanced_accuracy: 0.6338 - specificity: 0.9872 - miss_rate: 0.7196 - fall_out: 0.0128 - mcc: 0.4118 - val_loss: 1.2617 - val_accuracy: 0.5700 - val_recall: 0.3200 - val_precision: 0.7805 - val_AUROC: 0.9216 - val_AUPRC: 0.6440 - val_f1_score: 0.4539 - val_balanced_accuracy: 0.6550 - val_specificity: 0.9900 - val_miss_rate: 0.6800 - val_fall_out: 0.0100 - val_mcc: 0.4690
Epoch 47/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4015 - accuracy: 0.4931 - recall: 0.2891 - precision: 0.6834 - AUROC: 0.8950 - AUPRC: 0.5511 - f1_score: 0.4063 - balanced_accuracy: 0.6371 - specificity: 0.9851 - miss_rate: 0.7109 - fall_out: 0.0149 - mcc: 0.4087 - val_loss: 1.2564 - val_accuracy: 0.5950 - val_recall: 0.3200 - val_precision: 0.7711 - val_AUROC: 0.9217 - val_AUPRC: 0.6439 - val_f1_score: 0.4523 - val_balanced_accuracy: 0.6547 - val_specificity: 0.9894 - val_miss_rate: 0.6800 - val_fall_out: 0.0106 - val_mcc: 0.4655
Epoch 48/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3818 - accuracy: 0.5194 - recall: 0.2954 - precision: 0.7024 - AUROC: 0.8950 - AUPRC: 0.5496 - f1_score: 0.4159 - balanced_accuracy: 0.6407 - specificity: 0.9861 - miss_rate: 0.7046 - fall_out: 0.0139 - mcc: 0.4207 - val_loss: 1.2546 - val_accuracy: 0.6000 - val_recall: 0.3050 - val_precision: 0.7722 - val_AUROC: 0.9212 - val_AUPRC: 0.6385 - val_f1_score: 0.4373 - val_balanced_accuracy: 0.6475 - val_specificity: 0.9900 - val_miss_rate: 0.6950 - val_fall_out: 0.0100 - val_mcc: 0.4544
Epoch 49/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4024 - accuracy: 0.5094 - recall: 0.2854 - precision: 0.6972 - AUROC: 0.8897 - AUPRC: 0.5475 - f1_score: 0.4050 - balanced_accuracy: 0.6358 - specificity: 0.9862 - miss_rate: 0.7146 - fall_out: 0.0138 - mcc: 0.4113 - val_loss: 1.2433 - val_accuracy: 0.5650 - val_recall: 0.3200 - val_precision: 0.7805 - val_AUROC: 0.9227 - val_AUPRC: 0.6413 - val_f1_score: 0.4539 - val_balanced_accuracy: 0.6550 - val_specificity: 0.9900 - val_miss_rate: 0.6800 - val_fall_out: 0.0100 - val_mcc: 0.4690
Epoch 50/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3032 - accuracy: 0.5407 - recall: 0.3267 - precision: 0.7479 - AUROC: 0.9056 - AUPRC: 0.5862 - f1_score: 0.4547 - balanced_accuracy: 0.6572 - specificity: 0.9878 - miss_rate: 0.6733 - fall_out: 0.0122 - mcc: 0.4615 - val_loss: 1.2388 - val_accuracy: 0.5550 - val_recall: 0.3300 - val_precision: 0.7674 - val_AUROC: 0.9234 - val_AUPRC: 0.6416 - val_f1_score: 0.4615 - val_balanced_accuracy: 0.6594 - val_specificity: 0.9889 - val_miss_rate: 0.6700 - val_fall_out: 0.0111 - val_mcc: 0.4716
Epoch 51/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3453 - accuracy: 0.5069 - recall: 0.3029 - precision: 0.7055 - AUROC: 0.8989 - AUPRC: 0.5514 - f1_score: 0.4238 - balanced_accuracy: 0.6444 - specificity: 0.9860 - miss_rate: 0.6971 - fall_out: 0.0140 - mcc: 0.4275 - val_loss: 1.2301 - val_accuracy: 0.5750 - val_recall: 0.3350 - val_precision: 0.7614 - val_AUROC: 0.9241 - val_AUPRC: 0.6476 - val_f1_score: 0.4653 - val_balanced_accuracy: 0.6617 - val_specificity: 0.9883 - val_miss_rate: 0.6650 - val_fall_out: 0.0117 - val_mcc: 0.4730
Epoch 52/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2927 - accuracy: 0.5532 - recall: 0.3304 - precision: 0.7521 - AUROC: 0.9086 - AUPRC: 0.5968 - f1_score: 0.4591 - balanced_accuracy: 0.6592 - specificity: 0.9879 - miss_rate: 0.6696 - fall_out: 0.0121 - mcc: 0.4660 - val_loss: 1.2154 - val_accuracy: 0.5750 - val_recall: 0.3500 - val_precision: 0.7692 - val_AUROC: 0.9247 - val_AUPRC: 0.6542 - val_f1_score: 0.4811 - val_balanced_accuracy: 0.6692 - val_specificity: 0.9883 - val_miss_rate: 0.6500 - val_fall_out: 0.0117 - val_mcc: 0.4870
Epoch 53/100
7/7 [==============================] - 0s 13ms/step - loss: 1.3079 - accuracy: 0.5369 - recall: 0.3267 - precision: 0.7331 - AUROC: 0.9047 - AUPRC: 0.5857 - f1_score: 0.4519 - balanced_accuracy: 0.6567 - specificity: 0.9868 - miss_rate: 0.6733 - fall_out: 0.0132 - mcc: 0.4558 - val_loss: 1.2043 - val_accuracy: 0.5800 - val_recall: 0.3550 - val_precision: 0.7802 - val_AUROC: 0.9255 - val_AUPRC: 0.6605 - val_f1_score: 0.4880 - val_balanced_accuracy: 0.6719 - val_specificity: 0.9889 - val_miss_rate: 0.6450 - val_fall_out: 0.0111 - val_mcc: 0.4950
Epoch 54/100
7/7 [==============================] - 0s 14ms/step - loss: 1.2887 - accuracy: 0.5557 - recall: 0.3404 - precision: 0.7177 - AUROC: 0.9078 - AUPRC: 0.6065 - f1_score: 0.4618 - balanced_accuracy: 0.6628 - specificity: 0.9851 - miss_rate: 0.6596 - fall_out: 0.0149 - mcc: 0.4595 - val_loss: 1.1857 - val_accuracy: 0.6000 - val_recall: 0.3600 - val_precision: 0.7826 - val_AUROC: 0.9278 - val_AUPRC: 0.6713 - val_f1_score: 0.4932 - val_balanced_accuracy: 0.6744 - val_specificity: 0.9889 - val_miss_rate: 0.6400 - val_fall_out: 0.0111 - val_mcc: 0.4996
Epoch 55/100
7/7 [==============================] - 0s 14ms/step - loss: 1.2916 - accuracy: 0.5557 - recall: 0.3367 - precision: 0.7005 - AUROC: 0.9096 - AUPRC: 0.5855 - f1_score: 0.4548 - balanced_accuracy: 0.6603 - specificity: 0.9840 - miss_rate: 0.6633 - fall_out: 0.0160 - mcc: 0.4498 - val_loss: 1.1837 - val_accuracy: 0.5900 - val_recall: 0.3650 - val_precision: 0.7604 - val_AUROC: 0.9278 - val_AUPRC: 0.6659 - val_f1_score: 0.4932 - val_balanced_accuracy: 0.6761 - val_specificity: 0.9872 - val_miss_rate: 0.6350 - val_fall_out: 0.0128 - val_mcc: 0.4943
Epoch 56/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3340 - accuracy: 0.5394 - recall: 0.3579 - precision: 0.7566 - AUROC: 0.9023 - AUPRC: 0.6009 - f1_score: 0.4860 - balanced_accuracy: 0.6726 - specificity: 0.9872 - miss_rate: 0.6421 - fall_out: 0.0128 - mcc: 0.4877 - val_loss: 1.1753 - val_accuracy: 0.6100 - val_recall: 0.3550 - val_precision: 0.7553 - val_AUROC: 0.9290 - val_AUPRC: 0.6671 - val_f1_score: 0.4830 - val_balanced_accuracy: 0.6711 - val_specificity: 0.9872 - val_miss_rate: 0.6450 - val_fall_out: 0.0128 - val_mcc: 0.4851
Epoch 57/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2270 - accuracy: 0.5707 - recall: 0.3492 - precision: 0.7304 - AUROC: 0.9175 - AUPRC: 0.6165 - f1_score: 0.4725 - balanced_accuracy: 0.6674 - specificity: 0.9857 - miss_rate: 0.6508 - fall_out: 0.0143 - mcc: 0.4708 - val_loss: 1.1642 - val_accuracy: 0.6050 - val_recall: 0.3700 - val_precision: 0.7629 - val_AUROC: 0.9303 - val_AUPRC: 0.6721 - val_f1_score: 0.4983 - val_balanced_accuracy: 0.6786 - val_specificity: 0.9872 - val_miss_rate: 0.6300 - val_fall_out: 0.0128 - val_mcc: 0.4989
Epoch 58/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3002 - accuracy: 0.5332 - recall: 0.3592 - precision: 0.7229 - AUROC: 0.9058 - AUPRC: 0.5977 - f1_score: 0.4799 - balanced_accuracy: 0.6720 - specificity: 0.9847 - miss_rate: 0.6408 - fall_out: 0.0153 - mcc: 0.4748 - val_loss: 1.1604 - val_accuracy: 0.5950 - val_recall: 0.3650 - val_precision: 0.7526 - val_AUROC: 0.9310 - val_AUPRC: 0.6734 - val_f1_score: 0.4916 - val_balanced_accuracy: 0.6758 - val_specificity: 0.9867 - val_miss_rate: 0.6350 - val_fall_out: 0.0133 - val_mcc: 0.4911
Epoch 59/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2262 - accuracy: 0.5920 - recall: 0.3805 - precision: 0.7600 - AUROC: 0.9171 - AUPRC: 0.6257 - f1_score: 0.5071 - balanced_accuracy: 0.6836 - specificity: 0.9866 - miss_rate: 0.6195 - fall_out: 0.0134 - mcc: 0.5050 - val_loss: 1.1542 - val_accuracy: 0.6050 - val_recall: 0.3750 - val_precision: 0.7653 - val_AUROC: 0.9310 - val_AUPRC: 0.6737 - val_f1_score: 0.5034 - val_balanced_accuracy: 0.6811 - val_specificity: 0.9872 - val_miss_rate: 0.6250 - val_fall_out: 0.0128 - val_mcc: 0.5034
Epoch 60/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2811 - accuracy: 0.5607 - recall: 0.3655 - precision: 0.7318 - AUROC: 0.9105 - AUPRC: 0.6030 - f1_score: 0.4875 - balanced_accuracy: 0.6753 - specificity: 0.9851 - miss_rate: 0.6345 - fall_out: 0.0149 - mcc: 0.4829 - val_loss: 1.1534 - val_accuracy: 0.6050 - val_recall: 0.3750 - val_precision: 0.7653 - val_AUROC: 0.9308 - val_AUPRC: 0.6755 - val_f1_score: 0.5034 - val_balanced_accuracy: 0.6811 - val_specificity: 0.9872 - val_miss_rate: 0.6250 - val_fall_out: 0.0128 - val_mcc: 0.5034
Epoch 61/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2545 - accuracy: 0.5970 - recall: 0.3605 - precision: 0.7619 - AUROC: 0.9118 - AUPRC: 0.6331 - f1_score: 0.4894 - balanced_accuracy: 0.6740 - specificity: 0.9875 - miss_rate: 0.6395 - fall_out: 0.0125 - mcc: 0.4917 - val_loss: 1.1486 - val_accuracy: 0.6150 - val_recall: 0.3750 - val_precision: 0.7812 - val_AUROC: 0.9305 - val_AUPRC: 0.6788 - val_f1_score: 0.5068 - val_balanced_accuracy: 0.6817 - val_specificity: 0.9883 - val_miss_rate: 0.6250 - val_fall_out: 0.0117 - val_mcc: 0.5099
Epoch 62/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2038 - accuracy: 0.5857 - recall: 0.3492 - precision: 0.7541 - AUROC: 0.9213 - AUPRC: 0.6342 - f1_score: 0.4773 - balanced_accuracy: 0.6683 - specificity: 0.9873 - miss_rate: 0.6508 - fall_out: 0.0127 - mcc: 0.4804 - val_loss: 1.1414 - val_accuracy: 0.6200 - val_recall: 0.3800 - val_precision: 0.7755 - val_AUROC: 0.9313 - val_AUPRC: 0.6816 - val_f1_score: 0.5101 - val_balanced_accuracy: 0.6839 - val_specificity: 0.9878 - val_miss_rate: 0.6200 - val_fall_out: 0.0122 - val_mcc: 0.5111
Epoch 63/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2181 - accuracy: 0.5695 - recall: 0.3717 - precision: 0.7519 - AUROC: 0.9175 - AUPRC: 0.6315 - f1_score: 0.4975 - balanced_accuracy: 0.6790 - specificity: 0.9864 - miss_rate: 0.6283 - fall_out: 0.0136 - mcc: 0.4956 - val_loss: 1.1398 - val_accuracy: 0.6050 - val_recall: 0.4000 - val_precision: 0.7767 - val_AUROC: 0.9315 - val_AUPRC: 0.6825 - val_f1_score: 0.5281 - val_balanced_accuracy: 0.6936 - val_specificity: 0.9872 - val_miss_rate: 0.6000 - val_fall_out: 0.0128 - val_mcc: 0.5256
Epoch 64/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2074 - accuracy: 0.5870 - recall: 0.3805 - precision: 0.7361 - AUROC: 0.9193 - AUPRC: 0.6250 - f1_score: 0.5017 - balanced_accuracy: 0.6827 - specificity: 0.9848 - miss_rate: 0.6195 - fall_out: 0.0152 - mcc: 0.4950 - val_loss: 1.1391 - val_accuracy: 0.6000 - val_recall: 0.4100 - val_precision: 0.7593 - val_AUROC: 0.9315 - val_AUPRC: 0.6812 - val_f1_score: 0.5325 - val_balanced_accuracy: 0.6978 - val_specificity: 0.9856 - val_miss_rate: 0.5900 - val_fall_out: 0.0144 - val_mcc: 0.5250
Epoch 65/100
7/7 [==============================] - 0s 13ms/step - loss: 1.2284 - accuracy: 0.5620 - recall: 0.3805 - precision: 0.7397 - AUROC: 0.9165 - AUPRC: 0.6225 - f1_score: 0.5025 - balanced_accuracy: 0.6828 - specificity: 0.9851 - miss_rate: 0.6195 - fall_out: 0.0149 - mcc: 0.4965 - val_loss: 1.1299 - val_accuracy: 0.6100 - val_recall: 0.4250 - val_precision: 0.7944 - val_AUROC: 0.9326 - val_AUPRC: 0.6889 - val_f1_score: 0.5537 - val_balanced_accuracy: 0.7064 - val_specificity: 0.9878 - val_miss_rate: 0.5750 - val_fall_out: 0.0122 - val_mcc: 0.5503
Epoch 66/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2234 - accuracy: 0.5757 - recall: 0.3892 - precision: 0.7422 - AUROC: 0.9166 - AUPRC: 0.6291 - f1_score: 0.5107 - balanced_accuracy: 0.6871 - specificity: 0.9850 - miss_rate: 0.6108 - fall_out: 0.0150 - mcc: 0.5036 - val_loss: 1.1196 - val_accuracy: 0.6100 - val_recall: 0.4150 - val_precision: 0.7981 - val_AUROC: 0.9350 - val_AUPRC: 0.6950 - val_f1_score: 0.5461 - val_balanced_accuracy: 0.7017 - val_specificity: 0.9883 - val_miss_rate: 0.5850 - val_fall_out: 0.0117 - val_mcc: 0.5450
Epoch 67/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1579 - accuracy: 0.6108 - recall: 0.4055 - precision: 0.7980 - AUROC: 0.9269 - AUPRC: 0.6635 - f1_score: 0.5378 - balanced_accuracy: 0.6971 - specificity: 0.9886 - miss_rate: 0.5945 - fall_out: 0.0114 - mcc: 0.5384 - val_loss: 1.1135 - val_accuracy: 0.6250 - val_recall: 0.4200 - val_precision: 0.7925 - val_AUROC: 0.9353 - val_AUPRC: 0.6981 - val_f1_score: 0.5490 - val_balanced_accuracy: 0.7039 - val_specificity: 0.9878 - val_miss_rate: 0.5800 - val_fall_out: 0.0122 - val_mcc: 0.5460
Epoch 68/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1817 - accuracy: 0.5945 - recall: 0.3992 - precision: 0.7780 - AUROC: 0.9243 - AUPRC: 0.6536 - f1_score: 0.5277 - balanced_accuracy: 0.6933 - specificity: 0.9873 - miss_rate: 0.6008 - fall_out: 0.0127 - mcc: 0.5257 - val_loss: 1.1080 - val_accuracy: 0.6200 - val_recall: 0.4250 - val_precision: 0.7870 - val_AUROC: 0.9358 - val_AUPRC: 0.6989 - val_f1_score: 0.5519 - val_balanced_accuracy: 0.7061 - val_specificity: 0.9872 - val_miss_rate: 0.5750 - val_fall_out: 0.0128 - val_mcc: 0.5472
Epoch 69/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1576 - accuracy: 0.6158 - recall: 0.3980 - precision: 0.7644 - AUROC: 0.9264 - AUPRC: 0.6656 - f1_score: 0.5235 - balanced_accuracy: 0.6922 - specificity: 0.9864 - miss_rate: 0.6020 - fall_out: 0.0136 - mcc: 0.5190 - val_loss: 1.1093 - val_accuracy: 0.6200 - val_recall: 0.4250 - val_precision: 0.7658 - val_AUROC: 0.9348 - val_AUPRC: 0.6957 - val_f1_score: 0.5466 - val_balanced_accuracy: 0.7053 - val_specificity: 0.9856 - val_miss_rate: 0.5750 - val_fall_out: 0.0144 - val_mcc: 0.5380
Epoch 70/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1203 - accuracy: 0.6120 - recall: 0.4218 - precision: 0.7694 - AUROC: 0.9305 - AUPRC: 0.6747 - f1_score: 0.5449 - balanced_accuracy: 0.7039 - specificity: 0.9860 - miss_rate: 0.5782 - fall_out: 0.0140 - mcc: 0.5374 - val_loss: 1.0999 - val_accuracy: 0.6150 - val_recall: 0.4350 - val_precision: 0.7699 - val_AUROC: 0.9363 - val_AUPRC: 0.7011 - val_f1_score: 0.5559 - val_balanced_accuracy: 0.7103 - val_specificity: 0.9856 - val_miss_rate: 0.5650 - val_fall_out: 0.0144 - val_mcc: 0.5464
Epoch 71/100
7/7 [==============================] - 0s 11ms/step - loss: 1.0658 - accuracy: 0.6195 - recall: 0.4193 - precision: 0.7648 - AUROC: 0.9389 - AUPRC: 0.6889 - f1_score: 0.5416 - balanced_accuracy: 0.7025 - specificity: 0.9857 - miss_rate: 0.5807 - fall_out: 0.0143 - mcc: 0.5337 - val_loss: 1.0933 - val_accuracy: 0.6150 - val_recall: 0.4350 - val_precision: 0.7768 - val_AUROC: 0.9375 - val_AUPRC: 0.7048 - val_f1_score: 0.5577 - val_balanced_accuracy: 0.7106 - val_specificity: 0.9861 - val_miss_rate: 0.5650 - val_fall_out: 0.0139 - val_mcc: 0.5495
Epoch 72/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1123 - accuracy: 0.5982 - recall: 0.4068 - precision: 0.7576 - AUROC: 0.9321 - AUPRC: 0.6740 - f1_score: 0.5293 - balanced_accuracy: 0.6961 - specificity: 0.9855 - miss_rate: 0.5932 - fall_out: 0.0145 - mcc: 0.5221 - val_loss: 1.0905 - val_accuracy: 0.6250 - val_recall: 0.4450 - val_precision: 0.7807 - val_AUROC: 0.9375 - val_AUPRC: 0.7054 - val_f1_score: 0.5669 - val_balanced_accuracy: 0.7156 - val_specificity: 0.9861 - val_miss_rate: 0.5550 - val_fall_out: 0.0139 - val_mcc: 0.5578
Epoch 73/100
7/7 [==============================] - 0s 12ms/step - loss: 1.0987 - accuracy: 0.5957 - recall: 0.4268 - precision: 0.7698 - AUROC: 0.9343 - AUPRC: 0.6802 - f1_score: 0.5491 - balanced_accuracy: 0.7063 - specificity: 0.9858 - miss_rate: 0.5732 - fall_out: 0.0142 - mcc: 0.5409 - val_loss: 1.0777 - val_accuracy: 0.6250 - val_recall: 0.4450 - val_precision: 0.7876 - val_AUROC: 0.9390 - val_AUPRC: 0.7104 - val_f1_score: 0.5687 - val_balanced_accuracy: 0.7158 - val_specificity: 0.9867 - val_miss_rate: 0.5550 - val_fall_out: 0.0133 - val_mcc: 0.5609
Epoch 74/100
7/7 [==============================] - 0s 11ms/step - loss: 1.1379 - accuracy: 0.5932 - recall: 0.4168 - precision: 0.7500 - AUROC: 0.9285 - AUPRC: 0.6707 - f1_score: 0.5358 - balanced_accuracy: 0.7007 - specificity: 0.9846 - miss_rate: 0.5832 - fall_out: 0.0154 - mcc: 0.5256 - val_loss: 1.0794 - val_accuracy: 0.6250 - val_recall: 0.4400 - val_precision: 0.7928 - val_AUROC: 0.9389 - val_AUPRC: 0.7073 - val_f1_score: 0.5659 - val_balanced_accuracy: 0.7136 - val_specificity: 0.9872 - val_miss_rate: 0.5600 - val_fall_out: 0.0128 - val_mcc: 0.5598
Epoch 75/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1005 - accuracy: 0.6120 - recall: 0.4443 - precision: 0.7717 - AUROC: 0.9318 - AUPRC: 0.6843 - f1_score: 0.5639 - balanced_accuracy: 0.7149 - specificity: 0.9854 - miss_rate: 0.5557 - fall_out: 0.0146 - mcc: 0.5534 - val_loss: 1.0726 - val_accuracy: 0.6250 - val_recall: 0.4400 - val_precision: 0.7788 - val_AUROC: 0.9388 - val_AUPRC: 0.7099 - val_f1_score: 0.5623 - val_balanced_accuracy: 0.7131 - val_specificity: 0.9861 - val_miss_rate: 0.5600 - val_fall_out: 0.0139 - val_mcc: 0.5537
Epoch 76/100
7/7 [==============================] - 0s 12ms/step - loss: 1.0459 - accuracy: 0.6395 - recall: 0.4456 - precision: 0.7859 - AUROC: 0.9419 - AUPRC: 0.7122 - f1_score: 0.5687 - balanced_accuracy: 0.7160 - specificity: 0.9865 - miss_rate: 0.5544 - fall_out: 0.0135 - mcc: 0.5605 - val_loss: 1.0805 - val_accuracy: 0.6250 - val_recall: 0.4400 - val_precision: 0.7652 - val_AUROC: 0.9374 - val_AUPRC: 0.7090 - val_f1_score: 0.5587 - val_balanced_accuracy: 0.7125 - val_specificity: 0.9850 - val_miss_rate: 0.5600 - val_fall_out: 0.0150 - val_mcc: 0.5477
Epoch 77/100
7/7 [==============================] - 0s 14ms/step - loss: 1.0801 - accuracy: 0.6308 - recall: 0.4518 - precision: 0.7814 - AUROC: 0.9368 - AUPRC: 0.6963 - f1_score: 0.5726 - balanced_accuracy: 0.7189 - specificity: 0.9860 - miss_rate: 0.5482 - fall_out: 0.0140 - mcc: 0.5627 - val_loss: 1.0780 - val_accuracy: 0.6300 - val_recall: 0.4450 - val_precision: 0.7739 - val_AUROC: 0.9377 - val_AUPRC: 0.7068 - val_f1_score: 0.5651 - val_balanced_accuracy: 0.7153 - val_specificity: 0.9856 - val_miss_rate: 0.5550 - val_fall_out: 0.0144 - val_mcc: 0.5549
25/25 [==============================] - 0s 5ms/step - loss: 0.7425 - accuracy: 0.7772 - recall: 0.5795 - precision: 0.9008 - AUROC: 0.9762 - AUPRC: 0.8595 - f1_score: 0.7053 - balanced_accuracy: 0.7862 - specificity: 0.9929 - miss_rate: 0.4205 - fall_out: 0.0071 - mcc: 0.6999
7/7 [==============================] - 0s 5ms/step - loss: 1.0780 - accuracy: 0.6300 - recall: 0.4450 - precision: 0.7739 - AUROC: 0.9377 - AUPRC: 0.7068 - f1_score: 0.5651 - balanced_accuracy: 0.7153 - specificity: 0.9856 - miss_rate: 0.5550 - fall_out: 0.0144 - mcc: 0.5549
5it [00:44, 9.03s/it]
-- HOLDOUT 6 -- WINDOW window_30s
-- 22 Uncorrelated features: [Pearson+Spearman]
['mfcc16_mean', 'harmony_mean', 'mfcc10_var', 'mfcc4_mean', 'mfcc15_mean', 'mfcc19_mean', 'mfcc8_var', 'mfcc9_var', 'mfcc3_mean', 'chroma_stft_var', 'mfcc3_var', 'mfcc1_var', 'mfcc2_var', 'mfcc7_var', 'tempo', 'mfcc13_mean', 'mfcc5_mean', 'mfcc17_mean', 'mfcc14_mean', 'perceptr_mean', 'mfcc5_var', 'rms_var']
-- Actually uncorrelated features: [confirmed via MIC]
[]
-- 1 Correlated features: [Pearson]
['spectral_centroid_mean']
Model: "MLP"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_200 (Dense) (None, 128) 7296
dropout_155 (Dropout) (None, 128) 0
dense_201 (Dense) (None, 64) 8256
dropout_156 (Dropout) (None, 64) 0
dense_202 (Dense) (None, 64) 4160
dropout_157 (Dropout) (None, 64) 0
dense_203 (Dense) (None, 10) 650
=================================================================
Total params: 20,362
Trainable params: 20,362
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
7/7 [==============================] - 2s 85ms/step - loss: 2.9418 - accuracy: 0.1151 - recall: 0.0125 - precision: 0.1111 - AUROC: 0.5113 - AUPRC: 0.1025 - f1_score: 0.0225 - balanced_accuracy: 0.5007 - specificity: 0.9889 - miss_rate: 0.9875 - fall_out: 0.0111 - mcc: 0.0040 - val_loss: 2.3322 - val_accuracy: 0.1200 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5879 - val_AUPRC: 0.1269 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.4997 - val_specificity: 0.9994 - val_miss_rate: 1.0000 - val_fall_out: 5.5556e-04 - val_mcc: -0.0075
Epoch 2/100
7/7 [==============================] - 0s 16ms/step - loss: 2.5930 - accuracy: 0.1364 - recall: 0.0100 - precision: 0.1739 - AUROC: 0.5543 - AUPRC: 0.1199 - f1_score: 0.0189 - balanced_accuracy: 0.5024 - specificity: 0.9947 - miss_rate: 0.9900 - fall_out: 0.0053 - mcc: 0.0187 - val_loss: 2.2708 - val_accuracy: 0.1800 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6434 - val_AUPRC: 0.1570 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.4994 - val_specificity: 0.9989 - val_miss_rate: 1.0000 - val_fall_out: 0.0011 - val_mcc: -0.0105
Epoch 3/100
7/7 [==============================] - 0s 15ms/step - loss: 2.4966 - accuracy: 0.1364 - recall: 0.0063 - precision: 0.1020 - AUROC: 0.5671 - AUPRC: 0.1233 - f1_score: 0.0118 - balanced_accuracy: 0.5001 - specificity: 0.9939 - miss_rate: 0.9937 - fall_out: 0.0061 - mcc: 5.3437e-04 - val_loss: 2.2092 - val_accuracy: 0.2850 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6882 - val_AUPRC: 0.2119 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.4994 - val_specificity: 0.9989 - val_miss_rate: 1.0000 - val_fall_out: 0.0011 - val_mcc: -0.0105
Epoch 4/100
7/7 [==============================] - 0s 15ms/step - loss: 2.4077 - accuracy: 0.1464 - recall: 0.0025 - precision: 0.0741 - AUROC: 0.5975 - AUPRC: 0.1377 - f1_score: 0.0048 - balanced_accuracy: 0.4995 - specificity: 0.9965 - miss_rate: 0.9975 - fall_out: 0.0035 - mcc: -0.0050 - val_loss: 2.1587 - val_accuracy: 0.3200 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7164 - val_AUPRC: 0.2610 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.4997 - val_specificity: 0.9994 - val_miss_rate: 1.0000 - val_fall_out: 5.5556e-04 - val_mcc: -0.0075
Epoch 5/100
7/7 [==============================] - 0s 14ms/step - loss: 2.3536 - accuracy: 0.1840 - recall: 0.0050 - precision: 0.1538 - AUROC: 0.6236 - AUPRC: 0.1548 - f1_score: 0.0097 - balanced_accuracy: 0.5010 - specificity: 0.9969 - miss_rate: 0.9950 - fall_out: 0.0031 - mcc: 0.0103 - val_loss: 2.1207 - val_accuracy: 0.3150 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7306 - val_AUPRC: 0.2826 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 6/100
7/7 [==============================] - 0s 13ms/step - loss: 2.3274 - accuracy: 0.1852 - recall: 0.0113 - precision: 0.2432 - AUROC: 0.6384 - AUPRC: 0.1736 - f1_score: 0.0215 - balanced_accuracy: 0.5037 - specificity: 0.9961 - miss_rate: 0.9887 - fall_out: 0.0039 - mcc: 0.0326 - val_loss: 2.0975 - val_accuracy: 0.3450 - val_recall: 0.0050 - val_precision: 0.5000 - val_AUROC: 0.7542 - val_AUPRC: 0.2935 - val_f1_score: 0.0099 - val_balanced_accuracy: 0.5022 - val_specificity: 0.9994 - val_miss_rate: 0.9950 - val_fall_out: 5.5556e-04 - val_mcc: 0.0422
Epoch 7/100
7/7 [==============================] - 0s 11ms/step - loss: 2.1689 - accuracy: 0.2340 - recall: 0.0138 - precision: 0.3438 - AUROC: 0.6661 - AUPRC: 0.1962 - f1_score: 0.0265 - balanced_accuracy: 0.5054 - specificity: 0.9971 - miss_rate: 0.9862 - fall_out: 0.0029 - mcc: 0.0515 - val_loss: 2.0493 - val_accuracy: 0.3500 - val_recall: 0.0050 - val_precision: 0.5000 - val_AUROC: 0.7655 - val_AUPRC: 0.3266 - val_f1_score: 0.0099 - val_balanced_accuracy: 0.5022 - val_specificity: 0.9994 - val_miss_rate: 0.9950 - val_fall_out: 5.5556e-04 - val_mcc: 0.0422
Epoch 8/100
7/7 [==============================] - 0s 12ms/step - loss: 2.1651 - accuracy: 0.2503 - recall: 0.0225 - precision: 0.4500 - AUROC: 0.6794 - AUPRC: 0.2105 - f1_score: 0.0429 - balanced_accuracy: 0.5097 - specificity: 0.9969 - miss_rate: 0.9775 - fall_out: 0.0031 - mcc: 0.0828 - val_loss: 2.0023 - val_accuracy: 0.3550 - val_recall: 0.0050 - val_precision: 1.0000 - val_AUROC: 0.7794 - val_AUPRC: 0.3464 - val_f1_score: 0.0100 - val_balanced_accuracy: 0.5025 - val_specificity: 1.0000 - val_miss_rate: 0.9950 - val_fall_out: 0.0000e+00 - val_mcc: 0.0671
Epoch 9/100
7/7 [==============================] - 0s 12ms/step - loss: 2.1283 - accuracy: 0.2691 - recall: 0.0313 - precision: 0.5000 - AUROC: 0.6984 - AUPRC: 0.2393 - f1_score: 0.0589 - balanced_accuracy: 0.5139 - specificity: 0.9965 - miss_rate: 0.9687 - fall_out: 0.0035 - mcc: 0.1058 - val_loss: 1.9614 - val_accuracy: 0.3600 - val_recall: 0.0150 - val_precision: 0.7500 - val_AUROC: 0.7929 - val_AUPRC: 0.3633 - val_f1_score: 0.0294 - val_balanced_accuracy: 0.5072 - val_specificity: 0.9994 - val_miss_rate: 0.9850 - val_fall_out: 5.5556e-04 - val_mcc: 0.0970
Epoch 10/100
7/7 [==============================] - 0s 12ms/step - loss: 2.0764 - accuracy: 0.2591 - recall: 0.0350 - precision: 0.5833 - AUROC: 0.7146 - AUPRC: 0.2437 - f1_score: 0.0661 - balanced_accuracy: 0.5161 - specificity: 0.9972 - miss_rate: 0.9650 - fall_out: 0.0028 - mcc: 0.1253 - val_loss: 1.9162 - val_accuracy: 0.3700 - val_recall: 0.0200 - val_precision: 0.8000 - val_AUROC: 0.8082 - val_AUPRC: 0.3870 - val_f1_score: 0.0390 - val_balanced_accuracy: 0.5097 - val_specificity: 0.9994 - val_miss_rate: 0.9800 - val_fall_out: 5.5556e-04 - val_mcc: 0.1168
Epoch 11/100
7/7 [==============================] - 0s 13ms/step - loss: 2.0592 - accuracy: 0.2841 - recall: 0.0488 - precision: 0.5270 - AUROC: 0.7186 - AUPRC: 0.2558 - f1_score: 0.0893 - balanced_accuracy: 0.5220 - specificity: 0.9951 - miss_rate: 0.9512 - fall_out: 0.0049 - mcc: 0.1376 - val_loss: 1.8787 - val_accuracy: 0.3700 - val_recall: 0.0350 - val_precision: 0.7000 - val_AUROC: 0.8127 - val_AUPRC: 0.3926 - val_f1_score: 0.0667 - val_balanced_accuracy: 0.5167 - val_specificity: 0.9983 - val_miss_rate: 0.9650 - val_fall_out: 0.0017 - val_mcc: 0.1418
Epoch 12/100
7/7 [==============================] - 0s 12ms/step - loss: 2.0547 - accuracy: 0.2641 - recall: 0.0513 - precision: 0.5000 - AUROC: 0.7333 - AUPRC: 0.2559 - f1_score: 0.0931 - balanced_accuracy: 0.5228 - specificity: 0.9943 - miss_rate: 0.9487 - fall_out: 0.0057 - mcc: 0.1358 - val_loss: 1.8410 - val_accuracy: 0.3750 - val_recall: 0.0600 - val_precision: 0.8571 - val_AUROC: 0.8250 - val_AUPRC: 0.4143 - val_f1_score: 0.1121 - val_balanced_accuracy: 0.5294 - val_specificity: 0.9989 - val_miss_rate: 0.9400 - val_fall_out: 0.0011 - val_mcc: 0.2119
Epoch 13/100
7/7 [==============================] - 0s 12ms/step - loss: 2.0004 - accuracy: 0.2929 - recall: 0.0726 - precision: 0.5918 - AUROC: 0.7439 - AUPRC: 0.2887 - f1_score: 0.1293 - balanced_accuracy: 0.5335 - specificity: 0.9944 - miss_rate: 0.9274 - fall_out: 0.0056 - mcc: 0.1827 - val_loss: 1.8059 - val_accuracy: 0.3650 - val_recall: 0.0800 - val_precision: 0.8889 - val_AUROC: 0.8383 - val_AUPRC: 0.4337 - val_f1_score: 0.1468 - val_balanced_accuracy: 0.5394 - val_specificity: 0.9989 - val_miss_rate: 0.9200 - val_fall_out: 0.0011 - val_mcc: 0.2506
Epoch 14/100
7/7 [==============================] - 0s 12ms/step - loss: 1.9514 - accuracy: 0.3154 - recall: 0.0638 - precision: 0.5543 - AUROC: 0.7609 - AUPRC: 0.2953 - f1_score: 0.1145 - balanced_accuracy: 0.5291 - specificity: 0.9943 - miss_rate: 0.9362 - fall_out: 0.0057 - mcc: 0.1635 - val_loss: 1.7645 - val_accuracy: 0.3700 - val_recall: 0.1200 - val_precision: 0.8889 - val_AUROC: 0.8439 - val_AUPRC: 0.4475 - val_f1_score: 0.2115 - val_balanced_accuracy: 0.5592 - val_specificity: 0.9983 - val_miss_rate: 0.8800 - val_fall_out: 0.0017 - val_mcc: 0.3076
Epoch 15/100
7/7 [==============================] - 0s 12ms/step - loss: 1.8499 - accuracy: 0.3742 - recall: 0.1101 - precision: 0.6984 - AUROC: 0.7987 - AUPRC: 0.3646 - f1_score: 0.1903 - balanced_accuracy: 0.5524 - specificity: 0.9947 - miss_rate: 0.8899 - fall_out: 0.0053 - mcc: 0.2525 - val_loss: 1.7225 - val_accuracy: 0.3950 - val_recall: 0.1500 - val_precision: 0.8824 - val_AUROC: 0.8510 - val_AUPRC: 0.4635 - val_f1_score: 0.2564 - val_balanced_accuracy: 0.5739 - val_specificity: 0.9978 - val_miss_rate: 0.8500 - val_fall_out: 0.0022 - val_mcc: 0.3429
Epoch 16/100
7/7 [==============================] - 0s 12ms/step - loss: 1.9113 - accuracy: 0.3542 - recall: 0.1014 - precision: 0.6923 - AUROC: 0.7794 - AUPRC: 0.3451 - f1_score: 0.1769 - balanced_accuracy: 0.5482 - specificity: 0.9950 - miss_rate: 0.8986 - fall_out: 0.0050 - mcc: 0.2407 - val_loss: 1.6850 - val_accuracy: 0.4100 - val_recall: 0.1600 - val_precision: 0.8889 - val_AUROC: 0.8558 - val_AUPRC: 0.4782 - val_f1_score: 0.2712 - val_balanced_accuracy: 0.5789 - val_specificity: 0.9978 - val_miss_rate: 0.8400 - val_fall_out: 0.0022 - val_mcc: 0.3560
Epoch 17/100
7/7 [==============================] - 0s 11ms/step - loss: 1.8599 - accuracy: 0.3630 - recall: 0.1202 - precision: 0.5963 - AUROC: 0.7915 - AUPRC: 0.3538 - f1_score: 0.2000 - balanced_accuracy: 0.5556 - specificity: 0.9910 - miss_rate: 0.8798 - fall_out: 0.0090 - mcc: 0.2372 - val_loss: 1.6502 - val_accuracy: 0.4500 - val_recall: 0.1650 - val_precision: 0.8684 - val_AUROC: 0.8638 - val_AUPRC: 0.4984 - val_f1_score: 0.2773 - val_balanced_accuracy: 0.5811 - val_specificity: 0.9972 - val_miss_rate: 0.8350 - val_fall_out: 0.0028 - val_mcc: 0.3565
Epoch 18/100
7/7 [==============================] - 0s 12ms/step - loss: 1.8818 - accuracy: 0.3517 - recall: 0.1189 - precision: 0.5975 - AUROC: 0.7886 - AUPRC: 0.3333 - f1_score: 0.1983 - balanced_accuracy: 0.5550 - specificity: 0.9911 - miss_rate: 0.8811 - fall_out: 0.0089 - mcc: 0.2363 - val_loss: 1.6221 - val_accuracy: 0.4550 - val_recall: 0.1900 - val_precision: 0.9048 - val_AUROC: 0.8695 - val_AUPRC: 0.5050 - val_f1_score: 0.3140 - val_balanced_accuracy: 0.5939 - val_specificity: 0.9978 - val_miss_rate: 0.8100 - val_fall_out: 0.0022 - val_mcc: 0.3929
Epoch 19/100
7/7 [==============================] - 0s 11ms/step - loss: 1.8020 - accuracy: 0.3504 - recall: 0.1239 - precision: 0.6644 - AUROC: 0.8060 - AUPRC: 0.3707 - f1_score: 0.2089 - balanced_accuracy: 0.5585 - specificity: 0.9930 - miss_rate: 0.8761 - fall_out: 0.0070 - mcc: 0.2594 - val_loss: 1.5874 - val_accuracy: 0.4600 - val_recall: 0.1950 - val_precision: 0.8478 - val_AUROC: 0.8775 - val_AUPRC: 0.5213 - val_f1_score: 0.3171 - val_balanced_accuracy: 0.5956 - val_specificity: 0.9961 - val_miss_rate: 0.8050 - val_fall_out: 0.0039 - val_mcc: 0.3825
Epoch 20/100
7/7 [==============================] - 0s 12ms/step - loss: 1.8205 - accuracy: 0.3579 - recall: 0.1239 - precision: 0.5723 - AUROC: 0.8054 - AUPRC: 0.3574 - f1_score: 0.2037 - balanced_accuracy: 0.5568 - specificity: 0.9897 - miss_rate: 0.8761 - fall_out: 0.0103 - mcc: 0.2342 - val_loss: 1.5670 - val_accuracy: 0.4700 - val_recall: 0.2050 - val_precision: 0.8913 - val_AUROC: 0.8819 - val_AUPRC: 0.5252 - val_f1_score: 0.3333 - val_balanced_accuracy: 0.6011 - val_specificity: 0.9972 - val_miss_rate: 0.7950 - val_fall_out: 0.0028 - val_mcc: 0.4047
Epoch 21/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7054 - accuracy: 0.3755 - recall: 0.1389 - precision: 0.6033 - AUROC: 0.8291 - AUPRC: 0.3977 - f1_score: 0.2258 - balanced_accuracy: 0.5644 - specificity: 0.9898 - miss_rate: 0.8611 - fall_out: 0.0102 - mcc: 0.2576 - val_loss: 1.5380 - val_accuracy: 0.4550 - val_recall: 0.2150 - val_precision: 0.8431 - val_AUROC: 0.8839 - val_AUPRC: 0.5338 - val_f1_score: 0.3426 - val_balanced_accuracy: 0.6053 - val_specificity: 0.9956 - val_miss_rate: 0.7850 - val_fall_out: 0.0044 - val_mcc: 0.4007
Epoch 22/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7075 - accuracy: 0.4105 - recall: 0.1627 - precision: 0.6341 - AUROC: 0.8318 - AUPRC: 0.4224 - f1_score: 0.2590 - balanced_accuracy: 0.5761 - specificity: 0.9896 - miss_rate: 0.8373 - fall_out: 0.0104 - mcc: 0.2889 - val_loss: 1.5080 - val_accuracy: 0.4550 - val_recall: 0.2200 - val_precision: 0.8302 - val_AUROC: 0.8902 - val_AUPRC: 0.5469 - val_f1_score: 0.3478 - val_balanced_accuracy: 0.6075 - val_specificity: 0.9950 - val_miss_rate: 0.7800 - val_fall_out: 0.0050 - val_mcc: 0.4016
Epoch 23/100
7/7 [==============================] - 0s 12ms/step - loss: 1.6767 - accuracy: 0.4018 - recall: 0.1677 - precision: 0.7322 - AUROC: 0.8319 - AUPRC: 0.4324 - f1_score: 0.2729 - balanced_accuracy: 0.5804 - specificity: 0.9932 - miss_rate: 0.8323 - fall_out: 0.0068 - mcc: 0.3227 - val_loss: 1.4792 - val_accuracy: 0.4800 - val_recall: 0.2150 - val_precision: 0.7963 - val_AUROC: 0.8940 - val_AUPRC: 0.5585 - val_f1_score: 0.3386 - val_balanced_accuracy: 0.6044 - val_specificity: 0.9939 - val_miss_rate: 0.7850 - val_fall_out: 0.0061 - val_mcc: 0.3866
Epoch 24/100
7/7 [==============================] - 0s 12ms/step - loss: 1.6553 - accuracy: 0.4168 - recall: 0.1840 - precision: 0.7000 - AUROC: 0.8476 - AUPRC: 0.4449 - f1_score: 0.2914 - balanced_accuracy: 0.5876 - specificity: 0.9912 - miss_rate: 0.8160 - fall_out: 0.0088 - mcc: 0.3286 - val_loss: 1.4495 - val_accuracy: 0.4950 - val_recall: 0.2150 - val_precision: 0.7679 - val_AUROC: 0.8978 - val_AUPRC: 0.5686 - val_f1_score: 0.3359 - val_balanced_accuracy: 0.6039 - val_specificity: 0.9928 - val_miss_rate: 0.7850 - val_fall_out: 0.0072 - val_mcc: 0.3778
Epoch 25/100
7/7 [==============================] - 0s 13ms/step - loss: 1.6365 - accuracy: 0.4180 - recall: 0.1852 - precision: 0.6981 - AUROC: 0.8446 - AUPRC: 0.4427 - f1_score: 0.2928 - balanced_accuracy: 0.5882 - specificity: 0.9911 - miss_rate: 0.8148 - fall_out: 0.0089 - mcc: 0.3292 - val_loss: 1.4169 - val_accuracy: 0.5300 - val_recall: 0.2200 - val_precision: 0.8148 - val_AUROC: 0.9020 - val_AUPRC: 0.5851 - val_f1_score: 0.3465 - val_balanced_accuracy: 0.6072 - val_specificity: 0.9944 - val_miss_rate: 0.7800 - val_fall_out: 0.0056 - val_mcc: 0.3969
Epoch 26/100
7/7 [==============================] - 0s 12ms/step - loss: 1.6007 - accuracy: 0.4543 - recall: 0.1940 - precision: 0.7110 - AUROC: 0.8531 - AUPRC: 0.4794 - f1_score: 0.3048 - balanced_accuracy: 0.5926 - specificity: 0.9912 - miss_rate: 0.8060 - fall_out: 0.0088 - mcc: 0.3411 - val_loss: 1.3820 - val_accuracy: 0.5450 - val_recall: 0.2250 - val_precision: 0.7895 - val_AUROC: 0.9049 - val_AUPRC: 0.5972 - val_f1_score: 0.3502 - val_balanced_accuracy: 0.6092 - val_specificity: 0.9933 - val_miss_rate: 0.7750 - val_fall_out: 0.0067 - val_mcc: 0.3936
Epoch 27/100
7/7 [==============================] - 0s 13ms/step - loss: 1.5879 - accuracy: 0.4581 - recall: 0.2253 - precision: 0.6950 - AUROC: 0.8554 - AUPRC: 0.4757 - f1_score: 0.3403 - balanced_accuracy: 0.6071 - specificity: 0.9890 - miss_rate: 0.7747 - fall_out: 0.0110 - mcc: 0.3630 - val_loss: 1.3579 - val_accuracy: 0.5650 - val_recall: 0.2400 - val_precision: 0.8136 - val_AUROC: 0.9106 - val_AUPRC: 0.6090 - val_f1_score: 0.3707 - val_balanced_accuracy: 0.6169 - val_specificity: 0.9939 - val_miss_rate: 0.7600 - val_fall_out: 0.0061 - val_mcc: 0.4147
Epoch 28/100
7/7 [==============================] - 0s 14ms/step - loss: 1.5458 - accuracy: 0.4393 - recall: 0.2078 - precision: 0.6561 - AUROC: 0.8650 - AUPRC: 0.4740 - f1_score: 0.3156 - balanced_accuracy: 0.5978 - specificity: 0.9879 - miss_rate: 0.7922 - fall_out: 0.0121 - mcc: 0.3352 - val_loss: 1.3374 - val_accuracy: 0.5700 - val_recall: 0.2500 - val_precision: 0.7812 - val_AUROC: 0.9134 - val_AUPRC: 0.6119 - val_f1_score: 0.3788 - val_balanced_accuracy: 0.6211 - val_specificity: 0.9922 - val_miss_rate: 0.7500 - val_fall_out: 0.0078 - val_mcc: 0.4129
Epoch 29/100
7/7 [==============================] - 0s 14ms/step - loss: 1.5735 - accuracy: 0.4606 - recall: 0.2303 - precision: 0.6996 - AUROC: 0.8603 - AUPRC: 0.4787 - f1_score: 0.3465 - balanced_accuracy: 0.6097 - specificity: 0.9890 - miss_rate: 0.7697 - fall_out: 0.0110 - mcc: 0.3687 - val_loss: 1.3180 - val_accuracy: 0.5550 - val_recall: 0.2500 - val_precision: 0.7576 - val_AUROC: 0.9173 - val_AUPRC: 0.6182 - val_f1_score: 0.3759 - val_balanced_accuracy: 0.6206 - val_specificity: 0.9911 - val_miss_rate: 0.7500 - val_fall_out: 0.0089 - val_mcc: 0.4049
Epoch 30/100
7/7 [==============================] - 0s 12ms/step - loss: 1.5794 - accuracy: 0.4656 - recall: 0.2340 - precision: 0.7004 - AUROC: 0.8670 - AUPRC: 0.4957 - f1_score: 0.3508 - balanced_accuracy: 0.6115 - specificity: 0.9889 - miss_rate: 0.7660 - fall_out: 0.0111 - mcc: 0.3721 - val_loss: 1.3031 - val_accuracy: 0.5550 - val_recall: 0.2500 - val_precision: 0.7692 - val_AUROC: 0.9184 - val_AUPRC: 0.6257 - val_f1_score: 0.3774 - val_balanced_accuracy: 0.6208 - val_specificity: 0.9917 - val_miss_rate: 0.7500 - val_fall_out: 0.0083 - val_mcc: 0.4089
Epoch 31/100
7/7 [==============================] - 0s 12ms/step - loss: 1.5634 - accuracy: 0.4581 - recall: 0.2240 - precision: 0.6755 - AUROC: 0.8671 - AUPRC: 0.4939 - f1_score: 0.3365 - balanced_accuracy: 0.6060 - specificity: 0.9880 - miss_rate: 0.7760 - fall_out: 0.0120 - mcc: 0.3553 - val_loss: 1.2962 - val_accuracy: 0.5300 - val_recall: 0.2650 - val_precision: 0.7465 - val_AUROC: 0.9193 - val_AUPRC: 0.6209 - val_f1_score: 0.3911 - val_balanced_accuracy: 0.6275 - val_specificity: 0.9900 - val_miss_rate: 0.7350 - val_fall_out: 0.0100 - val_mcc: 0.4134
Epoch 32/100
7/7 [==============================] - 0s 12ms/step - loss: 1.5360 - accuracy: 0.4718 - recall: 0.2528 - precision: 0.7163 - AUROC: 0.8699 - AUPRC: 0.5020 - f1_score: 0.3737 - balanced_accuracy: 0.6208 - specificity: 0.9889 - miss_rate: 0.7472 - fall_out: 0.0111 - mcc: 0.3929 - val_loss: 1.2750 - val_accuracy: 0.5400 - val_recall: 0.2650 - val_precision: 0.7794 - val_AUROC: 0.9218 - val_AUPRC: 0.6321 - val_f1_score: 0.3955 - val_balanced_accuracy: 0.6283 - val_specificity: 0.9917 - val_miss_rate: 0.7350 - val_fall_out: 0.0083 - val_mcc: 0.4249
Epoch 33/100
7/7 [==============================] - 0s 12ms/step - loss: 1.5279 - accuracy: 0.4556 - recall: 0.2503 - precision: 0.7042 - AUROC: 0.8771 - AUPRC: 0.5126 - f1_score: 0.3693 - balanced_accuracy: 0.6193 - specificity: 0.9883 - miss_rate: 0.7497 - fall_out: 0.0117 - mcc: 0.3867 - val_loss: 1.2553 - val_accuracy: 0.5650 - val_recall: 0.2850 - val_precision: 0.7808 - val_AUROC: 0.9238 - val_AUPRC: 0.6422 - val_f1_score: 0.4176 - val_balanced_accuracy: 0.6381 - val_specificity: 0.9911 - val_miss_rate: 0.7150 - val_fall_out: 0.0089 - val_mcc: 0.4417
Epoch 34/100
7/7 [==============================] - 0s 13ms/step - loss: 1.5166 - accuracy: 0.4631 - recall: 0.2453 - precision: 0.6599 - AUROC: 0.8782 - AUPRC: 0.5048 - f1_score: 0.3577 - balanced_accuracy: 0.6156 - specificity: 0.9860 - miss_rate: 0.7547 - fall_out: 0.0140 - mcc: 0.3667 - val_loss: 1.2424 - val_accuracy: 0.5700 - val_recall: 0.3000 - val_precision: 0.7895 - val_AUROC: 0.9249 - val_AUPRC: 0.6455 - val_f1_score: 0.4348 - val_balanced_accuracy: 0.6456 - val_specificity: 0.9911 - val_miss_rate: 0.7000 - val_fall_out: 0.0089 - val_mcc: 0.4568
Epoch 35/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4250 - accuracy: 0.4768 - recall: 0.2541 - precision: 0.6835 - AUROC: 0.8877 - AUPRC: 0.5296 - f1_score: 0.3704 - balanced_accuracy: 0.6205 - specificity: 0.9869 - miss_rate: 0.7459 - fall_out: 0.0131 - mcc: 0.3822 - val_loss: 1.2230 - val_accuracy: 0.5800 - val_recall: 0.3150 - val_precision: 0.7975 - val_AUROC: 0.9276 - val_AUPRC: 0.6508 - val_f1_score: 0.4516 - val_balanced_accuracy: 0.6531 - val_specificity: 0.9911 - val_miss_rate: 0.6850 - val_fall_out: 0.0089 - val_mcc: 0.4715
Epoch 36/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4840 - accuracy: 0.4718 - recall: 0.2703 - precision: 0.6771 - AUROC: 0.8786 - AUPRC: 0.5198 - f1_score: 0.3864 - balanced_accuracy: 0.6280 - specificity: 0.9857 - miss_rate: 0.7297 - fall_out: 0.0143 - mcc: 0.3923 - val_loss: 1.2145 - val_accuracy: 0.5900 - val_recall: 0.3100 - val_precision: 0.7949 - val_AUROC: 0.9288 - val_AUPRC: 0.6574 - val_f1_score: 0.4460 - val_balanced_accuracy: 0.6506 - val_specificity: 0.9911 - val_miss_rate: 0.6900 - val_fall_out: 0.0089 - val_mcc: 0.4666
Epoch 37/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4493 - accuracy: 0.4944 - recall: 0.2653 - precision: 0.7114 - AUROC: 0.8818 - AUPRC: 0.5253 - f1_score: 0.3865 - balanced_accuracy: 0.6267 - specificity: 0.9880 - miss_rate: 0.7347 - fall_out: 0.0120 - mcc: 0.4011 - val_loss: 1.2168 - val_accuracy: 0.5950 - val_recall: 0.3050 - val_precision: 0.7821 - val_AUROC: 0.9277 - val_AUPRC: 0.6521 - val_f1_score: 0.4388 - val_balanced_accuracy: 0.6478 - val_specificity: 0.9906 - val_miss_rate: 0.6950 - val_fall_out: 0.0094 - val_mcc: 0.4580
Epoch 38/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4228 - accuracy: 0.5056 - recall: 0.2753 - precision: 0.7166 - AUROC: 0.8912 - AUPRC: 0.5508 - f1_score: 0.3978 - balanced_accuracy: 0.6316 - specificity: 0.9879 - miss_rate: 0.7247 - fall_out: 0.0121 - mcc: 0.4109 - val_loss: 1.2038 - val_accuracy: 0.5850 - val_recall: 0.3000 - val_precision: 0.7500 - val_AUROC: 0.9274 - val_AUPRC: 0.6541 - val_f1_score: 0.4286 - val_balanced_accuracy: 0.6444 - val_specificity: 0.9889 - val_miss_rate: 0.7000 - val_fall_out: 0.0111 - val_mcc: 0.4423
Epoch 39/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4384 - accuracy: 0.4956 - recall: 0.2866 - precision: 0.6982 - AUROC: 0.8788 - AUPRC: 0.5336 - f1_score: 0.4064 - balanced_accuracy: 0.6364 - specificity: 0.9862 - miss_rate: 0.7134 - fall_out: 0.0138 - mcc: 0.4125 - val_loss: 1.1982 - val_accuracy: 0.5850 - val_recall: 0.3150 - val_precision: 0.7778 - val_AUROC: 0.9285 - val_AUPRC: 0.6592 - val_f1_score: 0.4484 - val_balanced_accuracy: 0.6525 - val_specificity: 0.9900 - val_miss_rate: 0.6850 - val_fall_out: 0.0100 - val_mcc: 0.4642
Epoch 40/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3866 - accuracy: 0.5081 - recall: 0.3116 - precision: 0.7523 - AUROC: 0.8965 - AUPRC: 0.5632 - f1_score: 0.4407 - balanced_accuracy: 0.6501 - specificity: 0.9886 - miss_rate: 0.6884 - fall_out: 0.0114 - mcc: 0.4520 - val_loss: 1.1790 - val_accuracy: 0.6050 - val_recall: 0.3150 - val_precision: 0.7975 - val_AUROC: 0.9307 - val_AUPRC: 0.6690 - val_f1_score: 0.4516 - val_balanced_accuracy: 0.6531 - val_specificity: 0.9911 - val_miss_rate: 0.6850 - val_fall_out: 0.0089 - val_mcc: 0.4715
Epoch 41/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4070 - accuracy: 0.5094 - recall: 0.2979 - precision: 0.7368 - AUROC: 0.8907 - AUPRC: 0.5521 - f1_score: 0.4242 - balanced_accuracy: 0.6430 - specificity: 0.9882 - miss_rate: 0.7021 - fall_out: 0.0118 - mcc: 0.4357 - val_loss: 1.1730 - val_accuracy: 0.5750 - val_recall: 0.3250 - val_precision: 0.8025 - val_AUROC: 0.9312 - val_AUPRC: 0.6745 - val_f1_score: 0.4626 - val_balanced_accuracy: 0.6581 - val_specificity: 0.9911 - val_miss_rate: 0.6750 - val_fall_out: 0.0089 - val_mcc: 0.4811
Epoch 42/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3425 - accuracy: 0.5319 - recall: 0.3091 - precision: 0.7329 - AUROC: 0.9026 - AUPRC: 0.5860 - f1_score: 0.4349 - balanced_accuracy: 0.6483 - specificity: 0.9875 - miss_rate: 0.6909 - fall_out: 0.0125 - mcc: 0.4427 - val_loss: 1.1617 - val_accuracy: 0.5750 - val_recall: 0.3350 - val_precision: 0.7976 - val_AUROC: 0.9314 - val_AUPRC: 0.6780 - val_f1_score: 0.4718 - val_balanced_accuracy: 0.6628 - val_specificity: 0.9906 - val_miss_rate: 0.6650 - val_fall_out: 0.0094 - val_mcc: 0.4869
Epoch 43/100
7/7 [==============================] - 0s 13ms/step - loss: 1.3644 - accuracy: 0.5069 - recall: 0.3429 - precision: 0.7366 - AUROC: 0.8972 - AUPRC: 0.5751 - f1_score: 0.4680 - balanced_accuracy: 0.6647 - specificity: 0.9864 - miss_rate: 0.6571 - fall_out: 0.0136 - mcc: 0.4689 - val_loss: 1.1619 - val_accuracy: 0.5700 - val_recall: 0.3350 - val_precision: 0.7614 - val_AUROC: 0.9310 - val_AUPRC: 0.6717 - val_f1_score: 0.4653 - val_balanced_accuracy: 0.6617 - val_specificity: 0.9883 - val_miss_rate: 0.6650 - val_fall_out: 0.0117 - val_mcc: 0.4730
Epoch 44/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3462 - accuracy: 0.5181 - recall: 0.2991 - precision: 0.7050 - AUROC: 0.8993 - AUPRC: 0.5795 - f1_score: 0.4200 - balanced_accuracy: 0.6426 - specificity: 0.9861 - miss_rate: 0.7009 - fall_out: 0.0139 - mcc: 0.4245 - val_loss: 1.1443 - val_accuracy: 0.6050 - val_recall: 0.3600 - val_precision: 0.7912 - val_AUROC: 0.9330 - val_AUPRC: 0.6832 - val_f1_score: 0.4948 - val_balanced_accuracy: 0.6747 - val_specificity: 0.9894 - val_miss_rate: 0.6400 - val_fall_out: 0.0106 - val_mcc: 0.5030
Epoch 45/100
7/7 [==============================] - 0s 13ms/step - loss: 1.3373 - accuracy: 0.5244 - recall: 0.2854 - precision: 0.6909 - AUROC: 0.9019 - AUPRC: 0.5727 - f1_score: 0.4039 - balanced_accuracy: 0.6356 - specificity: 0.9858 - miss_rate: 0.7146 - fall_out: 0.0142 - mcc: 0.4088 - val_loss: 1.1394 - val_accuracy: 0.6100 - val_recall: 0.3550 - val_precision: 0.7802 - val_AUROC: 0.9331 - val_AUPRC: 0.6843 - val_f1_score: 0.4880 - val_balanced_accuracy: 0.6719 - val_specificity: 0.9889 - val_miss_rate: 0.6450 - val_fall_out: 0.0111 - val_mcc: 0.4950
Epoch 46/100
7/7 [==============================] - 0s 13ms/step - loss: 1.3586 - accuracy: 0.5307 - recall: 0.3116 - precision: 0.7217 - AUROC: 0.9023 - AUPRC: 0.5688 - f1_score: 0.4353 - balanced_accuracy: 0.6491 - specificity: 0.9866 - miss_rate: 0.6884 - fall_out: 0.0134 - mcc: 0.4403 - val_loss: 1.1292 - val_accuracy: 0.6050 - val_recall: 0.3700 - val_precision: 0.7957 - val_AUROC: 0.9339 - val_AUPRC: 0.6881 - val_f1_score: 0.5051 - val_balanced_accuracy: 0.6797 - val_specificity: 0.9894 - val_miss_rate: 0.6300 - val_fall_out: 0.0106 - val_mcc: 0.5121
Epoch 47/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3427 - accuracy: 0.5369 - recall: 0.3304 - precision: 0.7078 - AUROC: 0.8985 - AUPRC: 0.5764 - f1_score: 0.4505 - balanced_accuracy: 0.6576 - specificity: 0.9848 - miss_rate: 0.6696 - fall_out: 0.0152 - mcc: 0.4483 - val_loss: 1.1407 - val_accuracy: 0.6250 - val_recall: 0.3650 - val_precision: 0.8022 - val_AUROC: 0.9303 - val_AUPRC: 0.6802 - val_f1_score: 0.5017 - val_balanced_accuracy: 0.6775 - val_specificity: 0.9900 - val_miss_rate: 0.6350 - val_fall_out: 0.0100 - val_mcc: 0.5110
Epoch 48/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3287 - accuracy: 0.5382 - recall: 0.3292 - precision: 0.7367 - AUROC: 0.9014 - AUPRC: 0.5886 - f1_score: 0.4550 - balanced_accuracy: 0.6580 - specificity: 0.9869 - miss_rate: 0.6708 - fall_out: 0.0131 - mcc: 0.4590 - val_loss: 1.1449 - val_accuracy: 0.6050 - val_recall: 0.3850 - val_precision: 0.8105 - val_AUROC: 0.9303 - val_AUPRC: 0.6776 - val_f1_score: 0.5220 - val_balanced_accuracy: 0.6875 - val_specificity: 0.9900 - val_miss_rate: 0.6150 - val_fall_out: 0.0100 - val_mcc: 0.5289
25/25 [==============================] - 0s 5ms/step - loss: 0.9665 - accuracy: 0.7046 - recall: 0.4456 - precision: 0.8856 - AUROC: 0.9587 - AUPRC: 0.7805 - f1_score: 0.5928 - balanced_accuracy: 0.7196 - specificity: 0.9936 - miss_rate: 0.5544 - fall_out: 0.0064 - mcc: 0.6027
7/7 [==============================] - 0s 6ms/step - loss: 1.1449 - accuracy: 0.6050 - recall: 0.3850 - precision: 0.8105 - AUROC: 0.9303 - AUPRC: 0.6776 - f1_score: 0.5220 - balanced_accuracy: 0.6875 - specificity: 0.9900 - miss_rate: 0.6150 - fall_out: 0.0100 - mcc: 0.5289
6it [00:51, 8.21s/it]
-- HOLDOUT 7 -- WINDOW window_30s
-- 24 Uncorrelated features: [Pearson+Spearman]
['mfcc16_mean', 'harmony_mean', 'mfcc10_var', 'mfcc4_mean', 'mfcc15_mean', 'mfcc19_mean', 'mfcc6_mean', 'mfcc8_var', 'mfcc9_var', 'mfcc20_mean', 'mfcc3_mean', 'mfcc17_var', 'chroma_stft_var', 'mfcc3_var', 'mfcc1_var', 'mfcc2_var', 'mfcc7_var', 'tempo', 'mfcc13_mean', 'mfcc5_mean', 'mfcc17_mean', 'mfcc14_mean', 'perceptr_mean', 'mfcc5_var']
-- Actually uncorrelated features: [confirmed via MIC]
[]
-- 1 Correlated features: [Pearson]
['spectral_centroid_mean']
Model: "MLP"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_204 (Dense) (None, 128) 7296
dropout_158 (Dropout) (None, 128) 0
dense_205 (Dense) (None, 64) 8256
dropout_159 (Dropout) (None, 64) 0
dense_206 (Dense) (None, 64) 4160
dropout_160 (Dropout) (None, 64) 0
dense_207 (Dense) (None, 10) 650
=================================================================
Total params: 20,362
Trainable params: 20,362
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
7/7 [==============================] - 2s 85ms/step - loss: 2.8820 - accuracy: 0.1001 - recall: 0.0050 - precision: 0.0500 - AUROC: 0.5122 - AUPRC: 0.1020 - f1_score: 0.0091 - balanced_accuracy: 0.4972 - specificity: 0.9894 - miss_rate: 0.9950 - fall_out: 0.0106 - mcc: -0.0168 - val_loss: 2.3115 - val_accuracy: 0.1150 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5741 - val_AUPRC: 0.1261 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.4994 - val_specificity: 0.9989 - val_miss_rate: 1.0000 - val_fall_out: 0.0011 - val_mcc: -0.0105
Epoch 2/100
7/7 [==============================] - 0s 14ms/step - loss: 2.7433 - accuracy: 0.1026 - recall: 0.0038 - precision: 0.0638 - AUROC: 0.5217 - AUPRC: 0.1035 - f1_score: 0.0071 - balanced_accuracy: 0.4988 - specificity: 0.9939 - miss_rate: 0.9962 - fall_out: 0.0061 - mcc: -0.0093 - val_loss: 2.2528 - val_accuracy: 0.2150 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6435 - val_AUPRC: 0.1824 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.4997 - val_specificity: 0.9994 - val_miss_rate: 1.0000 - val_fall_out: 5.5556e-04 - val_mcc: -0.0075
Epoch 3/100
7/7 [==============================] - 0s 15ms/step - loss: 2.7170 - accuracy: 0.1289 - recall: 0.0075 - precision: 0.1132 - AUROC: 0.5542 - AUPRC: 0.1172 - f1_score: 0.0141 - balanced_accuracy: 0.5005 - specificity: 0.9935 - miss_rate: 0.9925 - fall_out: 0.0065 - mcc: 0.0036 - val_loss: 2.2064 - val_accuracy: 0.3000 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6864 - val_AUPRC: 0.2194 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.4997 - val_specificity: 0.9994 - val_miss_rate: 1.0000 - val_fall_out: 5.5556e-04 - val_mcc: -0.0075
Epoch 4/100
7/7 [==============================] - 0s 14ms/step - loss: 2.4868 - accuracy: 0.1552 - recall: 0.0063 - precision: 0.1316 - AUROC: 0.5967 - AUPRC: 0.1365 - f1_score: 0.0119 - balanced_accuracy: 0.5008 - specificity: 0.9954 - miss_rate: 0.9937 - fall_out: 0.0046 - mcc: 0.0073 - val_loss: 2.1617 - val_accuracy: 0.3200 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7182 - val_AUPRC: 0.2590 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.4994 - val_specificity: 0.9989 - val_miss_rate: 1.0000 - val_fall_out: 0.0011 - val_mcc: -0.0105
Epoch 5/100
7/7 [==============================] - 0s 14ms/step - loss: 2.4172 - accuracy: 0.1777 - recall: 0.0038 - precision: 0.1000 - AUROC: 0.6048 - AUPRC: 0.1418 - f1_score: 0.0072 - balanced_accuracy: 0.5000 - specificity: 0.9962 - miss_rate: 0.9962 - fall_out: 0.0038 - mcc: 0.0000e+00 - val_loss: 2.1284 - val_accuracy: 0.3250 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7381 - val_AUPRC: 0.2778 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.4994 - val_specificity: 0.9989 - val_miss_rate: 1.0000 - val_fall_out: 0.0011 - val_mcc: -0.0105
Epoch 6/100
7/7 [==============================] - 0s 14ms/step - loss: 2.3252 - accuracy: 0.2103 - recall: 0.0163 - precision: 0.3250 - AUROC: 0.6357 - AUPRC: 0.1697 - f1_score: 0.0310 - balanced_accuracy: 0.5063 - specificity: 0.9962 - miss_rate: 0.9837 - fall_out: 0.0038 - mcc: 0.0532 - val_loss: 2.0920 - val_accuracy: 0.3250 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7632 - val_AUPRC: 0.2949 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.4997 - val_specificity: 0.9994 - val_miss_rate: 1.0000 - val_fall_out: 5.5556e-04 - val_mcc: -0.0075
Epoch 7/100
7/7 [==============================] - 0s 14ms/step - loss: 2.2440 - accuracy: 0.2165 - recall: 0.0138 - precision: 0.3056 - AUROC: 0.6544 - AUPRC: 0.1817 - f1_score: 0.0263 - balanced_accuracy: 0.5051 - specificity: 0.9965 - miss_rate: 0.9862 - fall_out: 0.0035 - mcc: 0.0461 - val_loss: 2.0544 - val_accuracy: 0.3400 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7727 - val_AUPRC: 0.3100 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 8/100
7/7 [==============================] - 0s 12ms/step - loss: 2.2344 - accuracy: 0.2103 - recall: 0.0138 - precision: 0.2895 - AUROC: 0.6685 - AUPRC: 0.1913 - f1_score: 0.0263 - balanced_accuracy: 0.5050 - specificity: 0.9962 - miss_rate: 0.9862 - fall_out: 0.0038 - mcc: 0.0437 - val_loss: 2.0051 - val_accuracy: 0.3350 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7950 - val_AUPRC: 0.3341 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.4997 - val_specificity: 0.9994 - val_miss_rate: 1.0000 - val_fall_out: 5.5556e-04 - val_mcc: -0.0075
Epoch 9/100
7/7 [==============================] - 0s 11ms/step - loss: 2.0944 - accuracy: 0.2703 - recall: 0.0438 - precision: 0.6250 - AUROC: 0.7045 - AUPRC: 0.2507 - f1_score: 0.0819 - balanced_accuracy: 0.5204 - specificity: 0.9971 - miss_rate: 0.9562 - fall_out: 0.0029 - mcc: 0.1470 - val_loss: 1.9481 - val_accuracy: 0.3300 - val_recall: 0.0050 - val_precision: 0.5000 - val_AUROC: 0.8174 - val_AUPRC: 0.3671 - val_f1_score: 0.0099 - val_balanced_accuracy: 0.5022 - val_specificity: 0.9994 - val_miss_rate: 0.9950 - val_fall_out: 5.5556e-04 - val_mcc: 0.0422
Epoch 10/100
7/7 [==============================] - 0s 12ms/step - loss: 2.1138 - accuracy: 0.2441 - recall: 0.0388 - precision: 0.4697 - AUROC: 0.7054 - AUPRC: 0.2268 - f1_score: 0.0717 - balanced_accuracy: 0.5170 - specificity: 0.9951 - miss_rate: 0.9612 - fall_out: 0.0049 - mcc: 0.1125 - val_loss: 1.8961 - val_accuracy: 0.3500 - val_recall: 0.0250 - val_precision: 0.6250 - val_AUROC: 0.8287 - val_AUPRC: 0.3867 - val_f1_score: 0.0481 - val_balanced_accuracy: 0.5117 - val_specificity: 0.9983 - val_miss_rate: 0.9750 - val_fall_out: 0.0017 - val_mcc: 0.1109
Epoch 11/100
7/7 [==============================] - 0s 11ms/step - loss: 2.1089 - accuracy: 0.2666 - recall: 0.0426 - precision: 0.4928 - AUROC: 0.7244 - AUPRC: 0.2491 - f1_score: 0.0783 - balanced_accuracy: 0.5188 - specificity: 0.9951 - miss_rate: 0.9574 - fall_out: 0.0049 - mcc: 0.1222 - val_loss: 1.8476 - val_accuracy: 0.3600 - val_recall: 0.0300 - val_precision: 0.6667 - val_AUROC: 0.8343 - val_AUPRC: 0.4097 - val_f1_score: 0.0574 - val_balanced_accuracy: 0.5142 - val_specificity: 0.9983 - val_miss_rate: 0.9700 - val_fall_out: 0.0017 - val_mcc: 0.1270
Epoch 12/100
7/7 [==============================] - 0s 12ms/step - loss: 2.1139 - accuracy: 0.2390 - recall: 0.0513 - precision: 0.5062 - AUROC: 0.7213 - AUPRC: 0.2385 - f1_score: 0.0932 - balanced_accuracy: 0.5229 - specificity: 0.9944 - miss_rate: 0.9487 - fall_out: 0.0056 - mcc: 0.1370 - val_loss: 1.8043 - val_accuracy: 0.3600 - val_recall: 0.0600 - val_precision: 0.8000 - val_AUROC: 0.8425 - val_AUPRC: 0.4283 - val_f1_score: 0.1116 - val_balanced_accuracy: 0.5292 - val_specificity: 0.9983 - val_miss_rate: 0.9400 - val_fall_out: 0.0017 - val_mcc: 0.2028
Epoch 13/100
7/7 [==============================] - 0s 12ms/step - loss: 2.0397 - accuracy: 0.2816 - recall: 0.0751 - precision: 0.5941 - AUROC: 0.7376 - AUPRC: 0.2761 - f1_score: 0.1333 - balanced_accuracy: 0.5347 - specificity: 0.9943 - miss_rate: 0.9249 - fall_out: 0.0057 - mcc: 0.1863 - val_loss: 1.7708 - val_accuracy: 0.3800 - val_recall: 0.0800 - val_precision: 0.8421 - val_AUROC: 0.8476 - val_AUPRC: 0.4399 - val_f1_score: 0.1461 - val_balanced_accuracy: 0.5392 - val_specificity: 0.9983 - val_miss_rate: 0.9200 - val_fall_out: 0.0017 - val_mcc: 0.2423
Epoch 14/100
7/7 [==============================] - 0s 12ms/step - loss: 1.9949 - accuracy: 0.3091 - recall: 0.0688 - precision: 0.5612 - AUROC: 0.7444 - AUPRC: 0.2914 - f1_score: 0.1226 - balanced_accuracy: 0.5314 - specificity: 0.9940 - miss_rate: 0.9312 - fall_out: 0.0060 - mcc: 0.1713 - val_loss: 1.7361 - val_accuracy: 0.4000 - val_recall: 0.1000 - val_precision: 0.8696 - val_AUROC: 0.8567 - val_AUPRC: 0.4573 - val_f1_score: 0.1794 - val_balanced_accuracy: 0.5492 - val_specificity: 0.9983 - val_miss_rate: 0.9000 - val_fall_out: 0.0017 - val_mcc: 0.2767
Epoch 15/100
7/7 [==============================] - 0s 12ms/step - loss: 1.9566 - accuracy: 0.3091 - recall: 0.0939 - precision: 0.6250 - AUROC: 0.7636 - AUPRC: 0.3119 - f1_score: 0.1632 - balanced_accuracy: 0.5438 - specificity: 0.9937 - miss_rate: 0.9061 - fall_out: 0.0063 - mcc: 0.2161 - val_loss: 1.7049 - val_accuracy: 0.4050 - val_recall: 0.1150 - val_precision: 0.8846 - val_AUROC: 0.8614 - val_AUPRC: 0.4642 - val_f1_score: 0.2035 - val_balanced_accuracy: 0.5567 - val_specificity: 0.9983 - val_miss_rate: 0.8850 - val_fall_out: 0.0017 - val_mcc: 0.3002
Epoch 16/100
7/7 [==============================] - 0s 13ms/step - loss: 1.9556 - accuracy: 0.2916 - recall: 0.0901 - precision: 0.5496 - AUROC: 0.7724 - AUPRC: 0.3004 - f1_score: 0.1548 - balanced_accuracy: 0.5410 - specificity: 0.9918 - miss_rate: 0.9099 - fall_out: 0.0082 - mcc: 0.1935 - val_loss: 1.6717 - val_accuracy: 0.4400 - val_recall: 0.1300 - val_precision: 0.8667 - val_AUROC: 0.8678 - val_AUPRC: 0.4814 - val_f1_score: 0.2261 - val_balanced_accuracy: 0.5639 - val_specificity: 0.9978 - val_miss_rate: 0.8700 - val_fall_out: 0.0022 - val_mcc: 0.3154
Epoch 17/100
7/7 [==============================] - 0s 12ms/step - loss: 1.9331 - accuracy: 0.3154 - recall: 0.1051 - precision: 0.6316 - AUROC: 0.7766 - AUPRC: 0.3214 - f1_score: 0.1803 - balanced_accuracy: 0.5492 - specificity: 0.9932 - miss_rate: 0.8949 - fall_out: 0.0068 - mcc: 0.2305 - val_loss: 1.6457 - val_accuracy: 0.4450 - val_recall: 0.1400 - val_precision: 0.8485 - val_AUROC: 0.8718 - val_AUPRC: 0.4926 - val_f1_score: 0.2403 - val_balanced_accuracy: 0.5686 - val_specificity: 0.9972 - val_miss_rate: 0.8600 - val_fall_out: 0.0028 - val_mcc: 0.3232
Epoch 18/100
7/7 [==============================] - 0s 12ms/step - loss: 1.8897 - accuracy: 0.3254 - recall: 0.1101 - precision: 0.5946 - AUROC: 0.7810 - AUPRC: 0.3262 - f1_score: 0.1859 - balanced_accuracy: 0.5509 - specificity: 0.9917 - miss_rate: 0.8899 - fall_out: 0.0083 - mcc: 0.2265 - val_loss: 1.6256 - val_accuracy: 0.4400 - val_recall: 0.1600 - val_precision: 0.8421 - val_AUROC: 0.8733 - val_AUPRC: 0.4983 - val_f1_score: 0.2689 - val_balanced_accuracy: 0.5783 - val_specificity: 0.9967 - val_miss_rate: 0.8400 - val_fall_out: 0.0033 - val_mcc: 0.3443
Epoch 19/100
7/7 [==============================] - 0s 12ms/step - loss: 1.8798 - accuracy: 0.3442 - recall: 0.1101 - precision: 0.6197 - AUROC: 0.7953 - AUPRC: 0.3410 - f1_score: 0.1870 - balanced_accuracy: 0.5513 - specificity: 0.9925 - miss_rate: 0.8899 - fall_out: 0.0075 - mcc: 0.2330 - val_loss: 1.5959 - val_accuracy: 0.4450 - val_recall: 0.1700 - val_precision: 0.8718 - val_AUROC: 0.8761 - val_AUPRC: 0.5091 - val_f1_score: 0.2845 - val_balanced_accuracy: 0.5836 - val_specificity: 0.9972 - val_miss_rate: 0.8300 - val_fall_out: 0.0028 - val_mcc: 0.3628
Epoch 20/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7830 - accuracy: 0.3817 - recall: 0.1164 - precision: 0.5924 - AUROC: 0.8171 - AUPRC: 0.3694 - f1_score: 0.1946 - balanced_accuracy: 0.5537 - specificity: 0.9911 - miss_rate: 0.8836 - fall_out: 0.0089 - mcc: 0.2324 - val_loss: 1.5711 - val_accuracy: 0.4550 - val_recall: 0.1750 - val_precision: 0.8750 - val_AUROC: 0.8786 - val_AUPRC: 0.5152 - val_f1_score: 0.2917 - val_balanced_accuracy: 0.5861 - val_specificity: 0.9972 - val_miss_rate: 0.8250 - val_fall_out: 0.0028 - val_mcc: 0.3690
Epoch 21/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7750 - accuracy: 0.3842 - recall: 0.1514 - precision: 0.6954 - AUROC: 0.8166 - AUPRC: 0.3920 - f1_score: 0.2487 - balanced_accuracy: 0.5720 - specificity: 0.9926 - miss_rate: 0.8486 - fall_out: 0.0074 - mcc: 0.2961 - val_loss: 1.5402 - val_accuracy: 0.4550 - val_recall: 0.1800 - val_precision: 0.8571 - val_AUROC: 0.8849 - val_AUPRC: 0.5304 - val_f1_score: 0.2975 - val_balanced_accuracy: 0.5883 - val_specificity: 0.9967 - val_miss_rate: 0.8200 - val_fall_out: 0.0033 - val_mcc: 0.3696
Epoch 22/100
7/7 [==============================] - 0s 12ms/step - loss: 1.8170 - accuracy: 0.3554 - recall: 0.1176 - precision: 0.5949 - AUROC: 0.8105 - AUPRC: 0.3553 - f1_score: 0.1964 - balanced_accuracy: 0.5544 - specificity: 0.9911 - miss_rate: 0.8824 - fall_out: 0.0089 - mcc: 0.2343 - val_loss: 1.5203 - val_accuracy: 0.4600 - val_recall: 0.1850 - val_precision: 0.7872 - val_AUROC: 0.8862 - val_AUPRC: 0.5318 - val_f1_score: 0.2996 - val_balanced_accuracy: 0.5897 - val_specificity: 0.9944 - val_miss_rate: 0.8150 - val_fall_out: 0.0056 - val_mcc: 0.3554
Epoch 23/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7718 - accuracy: 0.3630 - recall: 0.1389 - precision: 0.6167 - AUROC: 0.8144 - AUPRC: 0.3681 - f1_score: 0.2268 - balanced_accuracy: 0.5647 - specificity: 0.9904 - miss_rate: 0.8611 - fall_out: 0.0096 - mcc: 0.2615 - val_loss: 1.4958 - val_accuracy: 0.4650 - val_recall: 0.1850 - val_precision: 0.7872 - val_AUROC: 0.8903 - val_AUPRC: 0.5449 - val_f1_score: 0.2996 - val_balanced_accuracy: 0.5897 - val_specificity: 0.9944 - val_miss_rate: 0.8150 - val_fall_out: 0.0056 - val_mcc: 0.3554
Epoch 24/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7646 - accuracy: 0.3880 - recall: 0.1414 - precision: 0.6175 - AUROC: 0.8225 - AUPRC: 0.3827 - f1_score: 0.2301 - balanced_accuracy: 0.5658 - specificity: 0.9903 - miss_rate: 0.8586 - fall_out: 0.0097 - mcc: 0.2641 - val_loss: 1.4818 - val_accuracy: 0.4650 - val_recall: 0.1900 - val_precision: 0.7755 - val_AUROC: 0.8928 - val_AUPRC: 0.5491 - val_f1_score: 0.3052 - val_balanced_accuracy: 0.5919 - val_specificity: 0.9939 - val_miss_rate: 0.8100 - val_fall_out: 0.0061 - val_mcc: 0.3568
Epoch 25/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7133 - accuracy: 0.3742 - recall: 0.1527 - precision: 0.6224 - AUROC: 0.8303 - AUPRC: 0.3962 - f1_score: 0.2452 - balanced_accuracy: 0.5712 - specificity: 0.9897 - miss_rate: 0.8473 - fall_out: 0.0103 - mcc: 0.2762 - val_loss: 1.4636 - val_accuracy: 0.4750 - val_recall: 0.2050 - val_precision: 0.8200 - val_AUROC: 0.8981 - val_AUPRC: 0.5613 - val_f1_score: 0.3280 - val_balanced_accuracy: 0.6000 - val_specificity: 0.9950 - val_miss_rate: 0.7950 - val_fall_out: 0.0050 - val_mcc: 0.3843
Epoch 26/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7161 - accuracy: 0.3967 - recall: 0.1414 - precision: 0.5947 - AUROC: 0.8325 - AUPRC: 0.4008 - f1_score: 0.2285 - balanced_accuracy: 0.5654 - specificity: 0.9893 - miss_rate: 0.8586 - fall_out: 0.0107 - mcc: 0.2574 - val_loss: 1.4439 - val_accuracy: 0.4950 - val_recall: 0.2000 - val_precision: 0.8163 - val_AUROC: 0.9025 - val_AUPRC: 0.5733 - val_f1_score: 0.3213 - val_balanced_accuracy: 0.5975 - val_specificity: 0.9950 - val_miss_rate: 0.8000 - val_fall_out: 0.0050 - val_mcc: 0.3784
Epoch 27/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7330 - accuracy: 0.3817 - recall: 0.1489 - precision: 0.5920 - AUROC: 0.8339 - AUPRC: 0.3996 - f1_score: 0.2380 - balanced_accuracy: 0.5688 - specificity: 0.9886 - miss_rate: 0.8511 - fall_out: 0.0114 - mcc: 0.2635 - val_loss: 1.4287 - val_accuracy: 0.5150 - val_recall: 0.2000 - val_precision: 0.8333 - val_AUROC: 0.9053 - val_AUPRC: 0.5821 - val_f1_score: 0.3226 - val_balanced_accuracy: 0.5978 - val_specificity: 0.9956 - val_miss_rate: 0.8000 - val_fall_out: 0.0044 - val_mcc: 0.3833
Epoch 28/100
7/7 [==============================] - 0s 13ms/step - loss: 1.6481 - accuracy: 0.4130 - recall: 0.1577 - precision: 0.6597 - AUROC: 0.8450 - AUPRC: 0.4278 - f1_score: 0.2545 - balanced_accuracy: 0.5743 - specificity: 0.9910 - miss_rate: 0.8423 - fall_out: 0.0090 - mcc: 0.2920 - val_loss: 1.4055 - val_accuracy: 0.5350 - val_recall: 0.2050 - val_precision: 0.8542 - val_AUROC: 0.9094 - val_AUPRC: 0.5918 - val_f1_score: 0.3306 - val_balanced_accuracy: 0.6006 - val_specificity: 0.9961 - val_miss_rate: 0.7950 - val_fall_out: 0.0039 - val_mcc: 0.3942
Epoch 29/100
7/7 [==============================] - 0s 14ms/step - loss: 1.5900 - accuracy: 0.4393 - recall: 0.1765 - precision: 0.6589 - AUROC: 0.8568 - AUPRC: 0.4597 - f1_score: 0.2784 - balanced_accuracy: 0.5832 - specificity: 0.9898 - miss_rate: 0.8235 - fall_out: 0.0102 - mcc: 0.3090 - val_loss: 1.3843 - val_accuracy: 0.5350 - val_recall: 0.2250 - val_precision: 0.8491 - val_AUROC: 0.9112 - val_AUPRC: 0.5996 - val_f1_score: 0.3557 - val_balanced_accuracy: 0.6103 - val_specificity: 0.9956 - val_miss_rate: 0.7750 - val_fall_out: 0.0044 - val_mcc: 0.4120
Epoch 30/100
7/7 [==============================] - 0s 13ms/step - loss: 1.6337 - accuracy: 0.4243 - recall: 0.1927 - precision: 0.6814 - AUROC: 0.8458 - AUPRC: 0.4479 - f1_score: 0.3005 - balanced_accuracy: 0.5914 - specificity: 0.9900 - miss_rate: 0.8073 - fall_out: 0.0100 - mcc: 0.3307 - val_loss: 1.3634 - val_accuracy: 0.5500 - val_recall: 0.2450 - val_precision: 0.8750 - val_AUROC: 0.9130 - val_AUPRC: 0.6088 - val_f1_score: 0.3828 - val_balanced_accuracy: 0.6206 - val_specificity: 0.9961 - val_miss_rate: 0.7550 - val_fall_out: 0.0039 - val_mcc: 0.4385
Epoch 31/100
7/7 [==============================] - 0s 12ms/step - loss: 1.6390 - accuracy: 0.4318 - recall: 0.1902 - precision: 0.6496 - AUROC: 0.8556 - AUPRC: 0.4539 - f1_score: 0.2943 - balanced_accuracy: 0.5894 - specificity: 0.9886 - miss_rate: 0.8098 - fall_out: 0.0114 - mcc: 0.3182 - val_loss: 1.3466 - val_accuracy: 0.5450 - val_recall: 0.2400 - val_precision: 0.8571 - val_AUROC: 0.9142 - val_AUPRC: 0.6109 - val_f1_score: 0.3750 - val_balanced_accuracy: 0.6178 - val_specificity: 0.9956 - val_miss_rate: 0.7600 - val_fall_out: 0.0044 - val_mcc: 0.4284
Epoch 32/100
7/7 [==============================] - 0s 12ms/step - loss: 1.6717 - accuracy: 0.4155 - recall: 0.1790 - precision: 0.6059 - AUROC: 0.8513 - AUPRC: 0.4236 - f1_score: 0.2763 - balanced_accuracy: 0.5830 - specificity: 0.9871 - miss_rate: 0.8210 - fall_out: 0.0129 - mcc: 0.2942 - val_loss: 1.3306 - val_accuracy: 0.5450 - val_recall: 0.2500 - val_precision: 0.8475 - val_AUROC: 0.9154 - val_AUPRC: 0.6180 - val_f1_score: 0.3861 - val_balanced_accuracy: 0.6225 - val_specificity: 0.9950 - val_miss_rate: 0.7500 - val_fall_out: 0.0050 - val_mcc: 0.4344
Epoch 33/100
7/7 [==============================] - 0s 12ms/step - loss: 1.5882 - accuracy: 0.4643 - recall: 0.2228 - precision: 0.6899 - AUROC: 0.8605 - AUPRC: 0.4807 - f1_score: 0.3368 - balanced_accuracy: 0.6058 - specificity: 0.9889 - miss_rate: 0.7772 - fall_out: 0.0111 - mcc: 0.3592 - val_loss: 1.3175 - val_accuracy: 0.5500 - val_recall: 0.2750 - val_precision: 0.8462 - val_AUROC: 0.9164 - val_AUPRC: 0.6216 - val_f1_score: 0.4151 - val_balanced_accuracy: 0.6347 - val_specificity: 0.9944 - val_miss_rate: 0.7250 - val_fall_out: 0.0056 - val_mcc: 0.4559
Epoch 34/100
7/7 [==============================] - 0s 12ms/step - loss: 1.5393 - accuracy: 0.4243 - recall: 0.2278 - precision: 0.6691 - AUROC: 0.8642 - AUPRC: 0.4830 - f1_score: 0.3399 - balanced_accuracy: 0.6076 - specificity: 0.9875 - miss_rate: 0.7722 - fall_out: 0.0125 - mcc: 0.3561 - val_loss: 1.3005 - val_accuracy: 0.5500 - val_recall: 0.2800 - val_precision: 0.8485 - val_AUROC: 0.9185 - val_AUPRC: 0.6309 - val_f1_score: 0.4211 - val_balanced_accuracy: 0.6372 - val_specificity: 0.9944 - val_miss_rate: 0.7200 - val_fall_out: 0.0056 - val_mcc: 0.4609
Epoch 35/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4859 - accuracy: 0.4593 - recall: 0.2278 - precision: 0.6408 - AUROC: 0.8750 - AUPRC: 0.4978 - f1_score: 0.3361 - balanced_accuracy: 0.6068 - specificity: 0.9858 - miss_rate: 0.7722 - fall_out: 0.0142 - mcc: 0.3461 - val_loss: 1.2876 - val_accuracy: 0.5700 - val_recall: 0.2750 - val_precision: 0.8462 - val_AUROC: 0.9193 - val_AUPRC: 0.6394 - val_f1_score: 0.4151 - val_balanced_accuracy: 0.6347 - val_specificity: 0.9944 - val_miss_rate: 0.7250 - val_fall_out: 0.0056 - val_mcc: 0.4559
Epoch 36/100
7/7 [==============================] - 0s 12ms/step - loss: 1.5573 - accuracy: 0.4393 - recall: 0.2153 - precision: 0.6667 - AUROC: 0.8620 - AUPRC: 0.4687 - f1_score: 0.3254 - balanced_accuracy: 0.6017 - specificity: 0.9880 - miss_rate: 0.7847 - fall_out: 0.0120 - mcc: 0.3450 - val_loss: 1.2758 - val_accuracy: 0.5600 - val_recall: 0.2850 - val_precision: 0.8261 - val_AUROC: 0.9211 - val_AUPRC: 0.6405 - val_f1_score: 0.4238 - val_balanced_accuracy: 0.6392 - val_specificity: 0.9933 - val_miss_rate: 0.7150 - val_fall_out: 0.0067 - val_mcc: 0.4575
Epoch 37/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4867 - accuracy: 0.4718 - recall: 0.2466 - precision: 0.6888 - AUROC: 0.8729 - AUPRC: 0.5115 - f1_score: 0.3631 - balanced_accuracy: 0.6171 - specificity: 0.9876 - miss_rate: 0.7534 - fall_out: 0.0124 - mcc: 0.3782 - val_loss: 1.2568 - val_accuracy: 0.5650 - val_recall: 0.2950 - val_precision: 0.8551 - val_AUROC: 0.9240 - val_AUPRC: 0.6513 - val_f1_score: 0.4387 - val_balanced_accuracy: 0.6447 - val_specificity: 0.9944 - val_miss_rate: 0.7050 - val_fall_out: 0.0056 - val_mcc: 0.4758
Epoch 38/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4940 - accuracy: 0.4568 - recall: 0.2378 - precision: 0.6714 - AUROC: 0.8770 - AUPRC: 0.4947 - f1_score: 0.3512 - balanced_accuracy: 0.6124 - specificity: 0.9871 - miss_rate: 0.7622 - fall_out: 0.0129 - mcc: 0.3650 - val_loss: 1.2423 - val_accuracy: 0.5700 - val_recall: 0.3150 - val_precision: 0.8514 - val_AUROC: 0.9253 - val_AUPRC: 0.6556 - val_f1_score: 0.4599 - val_balanced_accuracy: 0.6544 - val_specificity: 0.9939 - val_miss_rate: 0.6850 - val_fall_out: 0.0061 - val_mcc: 0.4909
Epoch 39/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4492 - accuracy: 0.5019 - recall: 0.2703 - precision: 0.7248 - AUROC: 0.8893 - AUPRC: 0.5317 - f1_score: 0.3938 - balanced_accuracy: 0.6295 - specificity: 0.9886 - miss_rate: 0.7297 - fall_out: 0.0114 - mcc: 0.4099 - val_loss: 1.2414 - val_accuracy: 0.5750 - val_recall: 0.2950 - val_precision: 0.8551 - val_AUROC: 0.9246 - val_AUPRC: 0.6530 - val_f1_score: 0.4387 - val_balanced_accuracy: 0.6447 - val_specificity: 0.9944 - val_miss_rate: 0.7050 - val_fall_out: 0.0056 - val_mcc: 0.4758
Epoch 40/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4831 - accuracy: 0.4731 - recall: 0.2441 - precision: 0.6678 - AUROC: 0.8751 - AUPRC: 0.5011 - f1_score: 0.3575 - balanced_accuracy: 0.6153 - specificity: 0.9865 - miss_rate: 0.7559 - fall_out: 0.0135 - mcc: 0.3686 - val_loss: 1.2305 - val_accuracy: 0.5750 - val_recall: 0.2950 - val_precision: 0.8194 - val_AUROC: 0.9263 - val_AUPRC: 0.6569 - val_f1_score: 0.4338 - val_balanced_accuracy: 0.6439 - val_specificity: 0.9928 - val_miss_rate: 0.7050 - val_fall_out: 0.0072 - val_mcc: 0.4634
Epoch 41/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4597 - accuracy: 0.4819 - recall: 0.2478 - precision: 0.6712 - AUROC: 0.8831 - AUPRC: 0.5021 - f1_score: 0.3620 - balanced_accuracy: 0.6172 - specificity: 0.9865 - miss_rate: 0.7522 - fall_out: 0.0135 - mcc: 0.3728 - val_loss: 1.2176 - val_accuracy: 0.5750 - val_recall: 0.3100 - val_precision: 0.8493 - val_AUROC: 0.9290 - val_AUPRC: 0.6640 - val_f1_score: 0.4542 - val_balanced_accuracy: 0.6519 - val_specificity: 0.9939 - val_miss_rate: 0.6900 - val_fall_out: 0.0061 - val_mcc: 0.4861
Epoch 42/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4460 - accuracy: 0.4894 - recall: 0.2541 - precision: 0.6905 - AUROC: 0.8864 - AUPRC: 0.5348 - f1_score: 0.3715 - balanced_accuracy: 0.6207 - specificity: 0.9873 - miss_rate: 0.7459 - fall_out: 0.0127 - mcc: 0.3847 - val_loss: 1.2034 - val_accuracy: 0.5850 - val_recall: 0.3150 - val_precision: 0.8400 - val_AUROC: 0.9303 - val_AUPRC: 0.6717 - val_f1_score: 0.4582 - val_balanced_accuracy: 0.6542 - val_specificity: 0.9933 - val_miss_rate: 0.6850 - val_fall_out: 0.0067 - val_mcc: 0.4869
Epoch 43/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3811 - accuracy: 0.5131 - recall: 0.2678 - precision: 0.7157 - AUROC: 0.8941 - AUPRC: 0.5520 - f1_score: 0.3898 - balanced_accuracy: 0.6280 - specificity: 0.9882 - miss_rate: 0.7322 - fall_out: 0.0118 - mcc: 0.4047 - val_loss: 1.1942 - val_accuracy: 0.5800 - val_recall: 0.3300 - val_precision: 0.8462 - val_AUROC: 0.9299 - val_AUPRC: 0.6706 - val_f1_score: 0.4748 - val_balanced_accuracy: 0.6617 - val_specificity: 0.9933 - val_miss_rate: 0.6700 - val_fall_out: 0.0067 - val_mcc: 0.5010
Epoch 44/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4338 - accuracy: 0.5031 - recall: 0.2678 - precision: 0.7431 - AUROC: 0.8919 - AUPRC: 0.5484 - f1_score: 0.3937 - balanced_accuracy: 0.6288 - specificity: 0.9897 - miss_rate: 0.7322 - fall_out: 0.0103 - mcc: 0.4145 - val_loss: 1.1784 - val_accuracy: 0.5750 - val_recall: 0.3450 - val_precision: 0.8415 - val_AUROC: 0.9312 - val_AUPRC: 0.6735 - val_f1_score: 0.4894 - val_balanced_accuracy: 0.6689 - val_specificity: 0.9928 - val_miss_rate: 0.6550 - val_fall_out: 0.0072 - val_mcc: 0.5110
Epoch 45/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3633 - accuracy: 0.5194 - recall: 0.2941 - precision: 0.7121 - AUROC: 0.8969 - AUPRC: 0.5657 - f1_score: 0.4163 - balanced_accuracy: 0.6405 - specificity: 0.9868 - miss_rate: 0.7059 - fall_out: 0.0132 - mcc: 0.4235 - val_loss: 1.1680 - val_accuracy: 0.5950 - val_recall: 0.3500 - val_precision: 0.8434 - val_AUROC: 0.9320 - val_AUPRC: 0.6777 - val_f1_score: 0.4947 - val_balanced_accuracy: 0.6714 - val_specificity: 0.9928 - val_miss_rate: 0.6500 - val_fall_out: 0.0072 - val_mcc: 0.5156
Epoch 46/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3775 - accuracy: 0.5144 - recall: 0.2891 - precision: 0.7380 - AUROC: 0.8957 - AUPRC: 0.5676 - f1_score: 0.4155 - balanced_accuracy: 0.6389 - specificity: 0.9886 - miss_rate: 0.7109 - fall_out: 0.0114 - mcc: 0.4294 - val_loss: 1.1626 - val_accuracy: 0.5900 - val_recall: 0.3550 - val_precision: 0.8353 - val_AUROC: 0.9324 - val_AUPRC: 0.6765 - val_f1_score: 0.4982 - val_balanced_accuracy: 0.6736 - val_specificity: 0.9922 - val_miss_rate: 0.6450 - val_fall_out: 0.0078 - val_mcc: 0.5164
Epoch 47/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3719 - accuracy: 0.5344 - recall: 0.3091 - precision: 0.7395 - AUROC: 0.8976 - AUPRC: 0.5720 - f1_score: 0.4360 - balanced_accuracy: 0.6485 - specificity: 0.9879 - miss_rate: 0.6909 - fall_out: 0.0121 - mcc: 0.4453 - val_loss: 1.1464 - val_accuracy: 0.6150 - val_recall: 0.3600 - val_precision: 0.8276 - val_AUROC: 0.9347 - val_AUPRC: 0.6850 - val_f1_score: 0.5017 - val_balanced_accuracy: 0.6758 - val_specificity: 0.9917 - val_miss_rate: 0.6400 - val_fall_out: 0.0083 - val_mcc: 0.5172
Epoch 48/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3149 - accuracy: 0.5232 - recall: 0.3254 - precision: 0.7407 - AUROC: 0.9024 - AUPRC: 0.5819 - f1_score: 0.4522 - balanced_accuracy: 0.6564 - specificity: 0.9873 - miss_rate: 0.6746 - fall_out: 0.0127 - mcc: 0.4578 - val_loss: 1.1367 - val_accuracy: 0.6100 - val_recall: 0.3550 - val_precision: 0.8256 - val_AUROC: 0.9361 - val_AUPRC: 0.6892 - val_f1_score: 0.4965 - val_balanced_accuracy: 0.6733 - val_specificity: 0.9917 - val_miss_rate: 0.6450 - val_fall_out: 0.0083 - val_mcc: 0.5127
Epoch 49/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3241 - accuracy: 0.5319 - recall: 0.3254 - precision: 0.7324 - AUROC: 0.9014 - AUPRC: 0.5837 - f1_score: 0.4506 - balanced_accuracy: 0.6561 - specificity: 0.9868 - miss_rate: 0.6746 - fall_out: 0.0132 - mcc: 0.4545 - val_loss: 1.1280 - val_accuracy: 0.6200 - val_recall: 0.3700 - val_precision: 0.8315 - val_AUROC: 0.9366 - val_AUPRC: 0.6917 - val_f1_score: 0.5121 - val_balanced_accuracy: 0.6808 - val_specificity: 0.9917 - val_miss_rate: 0.6300 - val_fall_out: 0.0083 - val_mcc: 0.5262
Epoch 50/100
7/7 [==============================] - 0s 15ms/step - loss: 1.3263 - accuracy: 0.5357 - recall: 0.3204 - precision: 0.7293 - AUROC: 0.9050 - AUPRC: 0.5848 - f1_score: 0.4452 - balanced_accuracy: 0.6536 - specificity: 0.9868 - miss_rate: 0.6796 - fall_out: 0.0132 - mcc: 0.4497 - val_loss: 1.1164 - val_accuracy: 0.6100 - val_recall: 0.3800 - val_precision: 0.8261 - val_AUROC: 0.9381 - val_AUPRC: 0.6957 - val_f1_score: 0.5205 - val_balanced_accuracy: 0.6856 - val_specificity: 0.9911 - val_miss_rate: 0.6200 - val_fall_out: 0.0089 - val_mcc: 0.5315
Epoch 51/100
7/7 [==============================] - 0s 14ms/step - loss: 1.3461 - accuracy: 0.5282 - recall: 0.3166 - precision: 0.7355 - AUROC: 0.9021 - AUPRC: 0.5805 - f1_score: 0.4427 - balanced_accuracy: 0.6520 - specificity: 0.9873 - miss_rate: 0.6834 - fall_out: 0.0127 - mcc: 0.4493 - val_loss: 1.1119 - val_accuracy: 0.6050 - val_recall: 0.3800 - val_precision: 0.8261 - val_AUROC: 0.9382 - val_AUPRC: 0.6954 - val_f1_score: 0.5205 - val_balanced_accuracy: 0.6856 - val_specificity: 0.9911 - val_miss_rate: 0.6200 - val_fall_out: 0.0089 - val_mcc: 0.5315
Epoch 52/100
7/7 [==============================] - 0s 14ms/step - loss: 1.3110 - accuracy: 0.5369 - recall: 0.3304 - precision: 0.7253 - AUROC: 0.9036 - AUPRC: 0.5775 - f1_score: 0.4540 - balanced_accuracy: 0.6583 - specificity: 0.9861 - miss_rate: 0.6696 - fall_out: 0.0139 - mcc: 0.4554 - val_loss: 1.0999 - val_accuracy: 0.6100 - val_recall: 0.3900 - val_precision: 0.8298 - val_AUROC: 0.9393 - val_AUPRC: 0.6999 - val_f1_score: 0.5306 - val_balanced_accuracy: 0.6906 - val_specificity: 0.9911 - val_miss_rate: 0.6100 - val_fall_out: 0.0089 - val_mcc: 0.5402
Epoch 53/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3005 - accuracy: 0.5432 - recall: 0.3229 - precision: 0.7227 - AUROC: 0.9101 - AUPRC: 0.5901 - f1_score: 0.4464 - balanced_accuracy: 0.6546 - specificity: 0.9862 - miss_rate: 0.6771 - fall_out: 0.0138 - mcc: 0.4489 - val_loss: 1.0900 - val_accuracy: 0.6250 - val_recall: 0.4050 - val_precision: 0.8351 - val_AUROC: 0.9405 - val_AUPRC: 0.7034 - val_f1_score: 0.5455 - val_balanced_accuracy: 0.6981 - val_specificity: 0.9911 - val_miss_rate: 0.5950 - val_fall_out: 0.0089 - val_mcc: 0.5532
Epoch 54/100
7/7 [==============================] - 0s 13ms/step - loss: 1.3272 - accuracy: 0.5282 - recall: 0.3116 - precision: 0.6917 - AUROC: 0.9041 - AUPRC: 0.5711 - f1_score: 0.4297 - balanced_accuracy: 0.6481 - specificity: 0.9846 - miss_rate: 0.6884 - fall_out: 0.0154 - mcc: 0.4284 - val_loss: 1.0902 - val_accuracy: 0.6150 - val_recall: 0.3950 - val_precision: 0.8229 - val_AUROC: 0.9405 - val_AUPRC: 0.7025 - val_f1_score: 0.5338 - val_balanced_accuracy: 0.6928 - val_specificity: 0.9906 - val_miss_rate: 0.6050 - val_fall_out: 0.0094 - val_mcc: 0.5411
Epoch 55/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2921 - accuracy: 0.5257 - recall: 0.3317 - precision: 0.7048 - AUROC: 0.9063 - AUPRC: 0.5964 - f1_score: 0.4511 - balanced_accuracy: 0.6581 - specificity: 0.9846 - miss_rate: 0.6683 - fall_out: 0.0154 - mcc: 0.4480 - val_loss: 1.0873 - val_accuracy: 0.6100 - val_recall: 0.3900 - val_precision: 0.8125 - val_AUROC: 0.9409 - val_AUPRC: 0.7040 - val_f1_score: 0.5270 - val_balanced_accuracy: 0.6900 - val_specificity: 0.9900 - val_miss_rate: 0.6100 - val_fall_out: 0.0100 - val_mcc: 0.5333
Epoch 56/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2937 - accuracy: 0.5594 - recall: 0.3354 - precision: 0.7243 - AUROC: 0.9071 - AUPRC: 0.6008 - f1_score: 0.4585 - balanced_accuracy: 0.6606 - specificity: 0.9858 - miss_rate: 0.6646 - fall_out: 0.0142 - mcc: 0.4586 - val_loss: 1.0866 - val_accuracy: 0.6250 - val_recall: 0.4000 - val_precision: 0.8163 - val_AUROC: 0.9407 - val_AUPRC: 0.7035 - val_f1_score: 0.5369 - val_balanced_accuracy: 0.6950 - val_specificity: 0.9900 - val_miss_rate: 0.6000 - val_fall_out: 0.0100 - val_mcc: 0.5420
Epoch 57/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2521 - accuracy: 0.5569 - recall: 0.3730 - precision: 0.7469 - AUROC: 0.9136 - AUPRC: 0.6196 - f1_score: 0.4975 - balanced_accuracy: 0.6795 - specificity: 0.9860 - miss_rate: 0.6270 - fall_out: 0.0140 - mcc: 0.4943 - val_loss: 1.0786 - val_accuracy: 0.6200 - val_recall: 0.4100 - val_precision: 0.8367 - val_AUROC: 0.9411 - val_AUPRC: 0.7074 - val_f1_score: 0.5503 - val_balanced_accuracy: 0.7006 - val_specificity: 0.9911 - val_miss_rate: 0.5900 - val_fall_out: 0.0089 - val_mcc: 0.5574
Epoch 58/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2395 - accuracy: 0.5557 - recall: 0.3667 - precision: 0.7571 - AUROC: 0.9168 - AUPRC: 0.6217 - f1_score: 0.4941 - balanced_accuracy: 0.6768 - specificity: 0.9869 - miss_rate: 0.6333 - fall_out: 0.0131 - mcc: 0.4942 - val_loss: 1.0657 - val_accuracy: 0.6200 - val_recall: 0.4150 - val_precision: 0.8300 - val_AUROC: 0.9429 - val_AUPRC: 0.7152 - val_f1_score: 0.5533 - val_balanced_accuracy: 0.7028 - val_specificity: 0.9906 - val_miss_rate: 0.5850 - val_fall_out: 0.0094 - val_mcc: 0.5582
Epoch 59/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2524 - accuracy: 0.5507 - recall: 0.3542 - precision: 0.7567 - AUROC: 0.9146 - AUPRC: 0.6190 - f1_score: 0.4825 - balanced_accuracy: 0.6708 - specificity: 0.9873 - miss_rate: 0.6458 - fall_out: 0.0127 - mcc: 0.4851 - val_loss: 1.0575 - val_accuracy: 0.6200 - val_recall: 0.4250 - val_precision: 0.8252 - val_AUROC: 0.9437 - val_AUPRC: 0.7167 - val_f1_score: 0.5611 - val_balanced_accuracy: 0.7075 - val_specificity: 0.9900 - val_miss_rate: 0.5750 - val_fall_out: 0.0100 - val_mcc: 0.5633
Epoch 60/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2152 - accuracy: 0.5732 - recall: 0.3730 - precision: 0.7469 - AUROC: 0.9234 - AUPRC: 0.6367 - f1_score: 0.4975 - balanced_accuracy: 0.6795 - specificity: 0.9860 - miss_rate: 0.6270 - fall_out: 0.0140 - mcc: 0.4943 - val_loss: 1.0487 - val_accuracy: 0.6200 - val_recall: 0.4300 - val_precision: 0.8269 - val_AUROC: 0.9446 - val_AUPRC: 0.7190 - val_f1_score: 0.5658 - val_balanced_accuracy: 0.7100 - val_specificity: 0.9900 - val_miss_rate: 0.5700 - val_fall_out: 0.0100 - val_mcc: 0.5675
Epoch 61/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1876 - accuracy: 0.5870 - recall: 0.3717 - precision: 0.7444 - AUROC: 0.9239 - AUPRC: 0.6355 - f1_score: 0.4958 - balanced_accuracy: 0.6788 - specificity: 0.9858 - miss_rate: 0.6283 - fall_out: 0.0142 - mcc: 0.4924 - val_loss: 1.0396 - val_accuracy: 0.6250 - val_recall: 0.4400 - val_precision: 0.8381 - val_AUROC: 0.9452 - val_AUPRC: 0.7253 - val_f1_score: 0.5770 - val_balanced_accuracy: 0.7153 - val_specificity: 0.9906 - val_miss_rate: 0.5600 - val_fall_out: 0.0094 - val_mcc: 0.5791
Epoch 62/100
7/7 [==============================] - 0s 13ms/step - loss: 1.2434 - accuracy: 0.5657 - recall: 0.3642 - precision: 0.7257 - AUROC: 0.9133 - AUPRC: 0.6247 - f1_score: 0.4850 - balanced_accuracy: 0.6745 - specificity: 0.9847 - miss_rate: 0.6358 - fall_out: 0.0153 - mcc: 0.4794 - val_loss: 1.0397 - val_accuracy: 0.6250 - val_recall: 0.4400 - val_precision: 0.8462 - val_AUROC: 0.9454 - val_AUPRC: 0.7241 - val_f1_score: 0.5789 - val_balanced_accuracy: 0.7156 - val_specificity: 0.9911 - val_miss_rate: 0.5600 - val_fall_out: 0.0089 - val_mcc: 0.5825
Epoch 63/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1544 - accuracy: 0.5907 - recall: 0.4030 - precision: 0.7722 - AUROC: 0.9281 - AUPRC: 0.6549 - f1_score: 0.5296 - balanced_accuracy: 0.6949 - specificity: 0.9868 - miss_rate: 0.5970 - fall_out: 0.0132 - mcc: 0.5258 - val_loss: 1.0268 - val_accuracy: 0.6350 - val_recall: 0.4400 - val_precision: 0.8381 - val_AUROC: 0.9469 - val_AUPRC: 0.7305 - val_f1_score: 0.5770 - val_balanced_accuracy: 0.7153 - val_specificity: 0.9906 - val_miss_rate: 0.5600 - val_fall_out: 0.0094 - val_mcc: 0.5791
Epoch 64/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1975 - accuracy: 0.5907 - recall: 0.3830 - precision: 0.7556 - AUROC: 0.9219 - AUPRC: 0.6437 - f1_score: 0.5083 - balanced_accuracy: 0.6846 - specificity: 0.9862 - miss_rate: 0.6170 - fall_out: 0.0138 - mcc: 0.5049 - val_loss: 1.0196 - val_accuracy: 0.6300 - val_recall: 0.4550 - val_precision: 0.8349 - val_AUROC: 0.9472 - val_AUPRC: 0.7348 - val_f1_score: 0.5890 - val_balanced_accuracy: 0.7225 - val_specificity: 0.9900 - val_miss_rate: 0.5450 - val_fall_out: 0.0100 - val_mcc: 0.5881
Epoch 65/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1639 - accuracy: 0.5932 - recall: 0.4080 - precision: 0.7581 - AUROC: 0.9252 - AUPRC: 0.6544 - f1_score: 0.5305 - balanced_accuracy: 0.6968 - specificity: 0.9855 - miss_rate: 0.5920 - fall_out: 0.0145 - mcc: 0.5232 - val_loss: 1.0129 - val_accuracy: 0.6150 - val_recall: 0.4600 - val_precision: 0.8440 - val_AUROC: 0.9472 - val_AUPRC: 0.7383 - val_f1_score: 0.5955 - val_balanced_accuracy: 0.7253 - val_specificity: 0.9906 - val_miss_rate: 0.5400 - val_fall_out: 0.0094 - val_mcc: 0.5954
Epoch 66/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2160 - accuracy: 0.5820 - recall: 0.3755 - precision: 0.7634 - AUROC: 0.9193 - AUPRC: 0.6293 - f1_score: 0.5034 - balanced_accuracy: 0.6813 - specificity: 0.9871 - miss_rate: 0.6245 - fall_out: 0.0129 - mcc: 0.5029 - val_loss: 1.0066 - val_accuracy: 0.6300 - val_recall: 0.4800 - val_precision: 0.8421 - val_AUROC: 0.9481 - val_AUPRC: 0.7392 - val_f1_score: 0.6115 - val_balanced_accuracy: 0.7350 - val_specificity: 0.9900 - val_miss_rate: 0.5200 - val_fall_out: 0.0100 - val_mcc: 0.6082
Epoch 67/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1929 - accuracy: 0.5782 - recall: 0.3742 - precision: 0.7419 - AUROC: 0.9225 - AUPRC: 0.6389 - f1_score: 0.4975 - balanced_accuracy: 0.6799 - specificity: 0.9855 - miss_rate: 0.6258 - fall_out: 0.0145 - mcc: 0.4932 - val_loss: 0.9978 - val_accuracy: 0.6450 - val_recall: 0.4700 - val_precision: 0.8319 - val_AUROC: 0.9494 - val_AUPRC: 0.7445 - val_f1_score: 0.6006 - val_balanced_accuracy: 0.7297 - val_specificity: 0.9894 - val_miss_rate: 0.5300 - val_fall_out: 0.0106 - val_mcc: 0.5970
Epoch 68/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1265 - accuracy: 0.6033 - recall: 0.4030 - precision: 0.7630 - AUROC: 0.9305 - AUPRC: 0.6682 - f1_score: 0.5274 - balanced_accuracy: 0.6945 - specificity: 0.9861 - miss_rate: 0.5970 - fall_out: 0.0139 - mcc: 0.5219 - val_loss: 0.9877 - val_accuracy: 0.6450 - val_recall: 0.4850 - val_precision: 0.8509 - val_AUROC: 0.9508 - val_AUPRC: 0.7508 - val_f1_score: 0.6178 - val_balanced_accuracy: 0.7378 - val_specificity: 0.9906 - val_miss_rate: 0.5150 - val_fall_out: 0.0094 - val_mcc: 0.6154
Epoch 69/100
7/7 [==============================] - 0s 12ms/step - loss: 1.0876 - accuracy: 0.6033 - recall: 0.4093 - precision: 0.7786 - AUROC: 0.9355 - AUPRC: 0.6826 - f1_score: 0.5365 - balanced_accuracy: 0.6982 - specificity: 0.9871 - miss_rate: 0.5907 - fall_out: 0.0129 - mcc: 0.5328 - val_loss: 0.9800 - val_accuracy: 0.6500 - val_recall: 0.4850 - val_precision: 0.8291 - val_AUROC: 0.9503 - val_AUPRC: 0.7499 - val_f1_score: 0.6120 - val_balanced_accuracy: 0.7369 - val_specificity: 0.9889 - val_miss_rate: 0.5150 - val_fall_out: 0.0111 - val_mcc: 0.6058
Epoch 70/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1151 - accuracy: 0.5995 - recall: 0.4043 - precision: 0.7654 - AUROC: 0.9319 - AUPRC: 0.6631 - f1_score: 0.5291 - balanced_accuracy: 0.6952 - specificity: 0.9862 - miss_rate: 0.5957 - fall_out: 0.0138 - mcc: 0.5238 - val_loss: 0.9700 - val_accuracy: 0.6450 - val_recall: 0.4850 - val_precision: 0.8291 - val_AUROC: 0.9511 - val_AUPRC: 0.7529 - val_f1_score: 0.6120 - val_balanced_accuracy: 0.7369 - val_specificity: 0.9889 - val_miss_rate: 0.5150 - val_fall_out: 0.0111 - val_mcc: 0.6058
Epoch 71/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1169 - accuracy: 0.6020 - recall: 0.4230 - precision: 0.7613 - AUROC: 0.9319 - AUPRC: 0.6819 - f1_score: 0.5438 - balanced_accuracy: 0.7041 - specificity: 0.9853 - miss_rate: 0.5770 - fall_out: 0.0147 - mcc: 0.5347 - val_loss: 0.9648 - val_accuracy: 0.6500 - val_recall: 0.4750 - val_precision: 0.8190 - val_AUROC: 0.9512 - val_AUPRC: 0.7537 - val_f1_score: 0.6013 - val_balanced_accuracy: 0.7317 - val_specificity: 0.9883 - val_miss_rate: 0.5250 - val_fall_out: 0.0117 - val_mcc: 0.5947
Epoch 72/100
7/7 [==============================] - 0s 12ms/step - loss: 1.1048 - accuracy: 0.6195 - recall: 0.4193 - precision: 0.7737 - AUROC: 0.9324 - AUPRC: 0.6808 - f1_score: 0.5438 - balanced_accuracy: 0.7028 - specificity: 0.9864 - miss_rate: 0.5807 - fall_out: 0.0136 - mcc: 0.5375 - val_loss: 0.9498 - val_accuracy: 0.6900 - val_recall: 0.5000 - val_precision: 0.8621 - val_AUROC: 0.9525 - val_AUPRC: 0.7631 - val_f1_score: 0.6329 - val_balanced_accuracy: 0.7456 - val_specificity: 0.9911 - val_miss_rate: 0.5000 - val_fall_out: 0.0089 - val_mcc: 0.6303
Epoch 73/100
7/7 [==============================] - 0s 14ms/step - loss: 1.1869 - accuracy: 0.6095 - recall: 0.4168 - precision: 0.7500 - AUROC: 0.9260 - AUPRC: 0.6571 - f1_score: 0.5358 - balanced_accuracy: 0.7007 - specificity: 0.9846 - miss_rate: 0.5832 - fall_out: 0.0154 - mcc: 0.5256 - val_loss: 0.9519 - val_accuracy: 0.6950 - val_recall: 0.4900 - val_precision: 0.8522 - val_AUROC: 0.9527 - val_AUPRC: 0.7634 - val_f1_score: 0.6222 - val_balanced_accuracy: 0.7403 - val_specificity: 0.9906 - val_miss_rate: 0.5100 - val_fall_out: 0.0094 - val_mcc: 0.6193
Epoch 74/100
7/7 [==============================] - 0s 13ms/step - loss: 1.1010 - accuracy: 0.6233 - recall: 0.4068 - precision: 0.7908 - AUROC: 0.9367 - AUPRC: 0.6964 - f1_score: 0.5372 - balanced_accuracy: 0.6974 - specificity: 0.9880 - miss_rate: 0.5932 - fall_out: 0.0120 - mcc: 0.5362 - val_loss: 0.9504 - val_accuracy: 0.6950 - val_recall: 0.4950 - val_precision: 0.8390 - val_AUROC: 0.9514 - val_AUPRC: 0.7619 - val_f1_score: 0.6226 - val_balanced_accuracy: 0.7422 - val_specificity: 0.9894 - val_miss_rate: 0.5050 - val_fall_out: 0.0106 - val_mcc: 0.6168
25/25 [==============================] - 0s 5ms/step - loss: 0.7465 - accuracy: 0.7672 - recall: 0.5870 - precision: 0.9232 - AUROC: 0.9762 - AUPRC: 0.8614 - f1_score: 0.7177 - balanced_accuracy: 0.7908 - specificity: 0.9946 - miss_rate: 0.4130 - fall_out: 0.0054 - mcc: 0.7150
7/7 [==============================] - 0s 5ms/step - loss: 0.9504 - accuracy: 0.6950 - recall: 0.4950 - precision: 0.8390 - AUROC: 0.9514 - AUPRC: 0.7619 - f1_score: 0.6226 - balanced_accuracy: 0.7422 - specificity: 0.9894 - miss_rate: 0.5050 - fall_out: 0.0106 - mcc: 0.6168
7it [01:00, 8.48s/it]
-- HOLDOUT 8 -- WINDOW window_30s
-- 22 Uncorrelated features: [Pearson+Spearman]
['mfcc16_mean', 'harmony_mean', 'mfcc10_var', 'mfcc4_mean', 'mfcc15_mean', 'mfcc19_mean', 'mfcc6_mean', 'mfcc8_var', 'mfcc9_var', 'mfcc3_mean', 'chroma_stft_var', 'mfcc3_var', 'mfcc1_var', 'mfcc2_var', 'mfcc7_var', 'tempo', 'mfcc13_mean', 'mfcc5_mean', 'mfcc17_mean', 'mfcc14_mean', 'perceptr_mean', 'mfcc5_var']
-- Actually uncorrelated features: [confirmed via MIC]
[]
-- 1 Correlated features: [Pearson]
['spectral_centroid_mean']
Model: "MLP"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_208 (Dense) (None, 128) 7296
dropout_161 (Dropout) (None, 128) 0
dense_209 (Dense) (None, 64) 8256
dropout_162 (Dropout) (None, 64) 0
dense_210 (Dense) (None, 64) 4160
dropout_163 (Dropout) (None, 64) 0
dense_211 (Dense) (None, 10) 650
=================================================================
Total params: 20,362
Trainable params: 20,362
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
7/7 [==============================] - 2s 84ms/step - loss: 2.7508 - accuracy: 0.1064 - recall: 0.0100 - precision: 0.1429 - AUROC: 0.5207 - AUPRC: 0.1076 - f1_score: 0.0187 - balanced_accuracy: 0.5017 - specificity: 0.9933 - miss_rate: 0.9900 - fall_out: 0.0067 - mcc: 0.0120 - val_loss: 2.2392 - val_accuracy: 0.1900 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6255 - val_AUPRC: 0.1600 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 2/100
7/7 [==============================] - 0s 16ms/step - loss: 2.6179 - accuracy: 0.1227 - recall: 0.0075 - precision: 0.1429 - AUROC: 0.5417 - AUPRC: 0.1127 - f1_score: 0.0143 - balanced_accuracy: 0.5013 - specificity: 0.9950 - miss_rate: 0.9925 - fall_out: 0.0050 - mcc: 0.0104 - val_loss: 2.1932 - val_accuracy: 0.2600 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6906 - val_AUPRC: 0.2065 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.4997 - val_specificity: 0.9994 - val_miss_rate: 1.0000 - val_fall_out: 5.5556e-04 - val_mcc: -0.0075
Epoch 3/100
7/7 [==============================] - 0s 16ms/step - loss: 2.4746 - accuracy: 0.1577 - recall: 0.0100 - precision: 0.2222 - AUROC: 0.5713 - AUPRC: 0.1299 - f1_score: 0.0192 - balanced_accuracy: 0.5031 - specificity: 0.9961 - miss_rate: 0.9900 - fall_out: 0.0039 - mcc: 0.0274 - val_loss: 2.1732 - val_accuracy: 0.3350 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7264 - val_AUPRC: 0.2448 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.4997 - val_specificity: 0.9994 - val_miss_rate: 1.0000 - val_fall_out: 5.5556e-04 - val_mcc: -0.0075
Epoch 4/100
7/7 [==============================] - 0s 15ms/step - loss: 2.3820 - accuracy: 0.2165 - recall: 0.0100 - precision: 0.2162 - AUROC: 0.6101 - AUPRC: 0.1537 - f1_score: 0.0191 - balanced_accuracy: 0.5030 - specificity: 0.9960 - miss_rate: 0.9900 - fall_out: 0.0040 - mcc: 0.0264 - val_loss: 2.1367 - val_accuracy: 0.3450 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7662 - val_AUPRC: 0.2899 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.4997 - val_specificity: 0.9994 - val_miss_rate: 1.0000 - val_fall_out: 5.5556e-04 - val_mcc: -0.0075
Epoch 5/100
7/7 [==============================] - 0s 15ms/step - loss: 2.3316 - accuracy: 0.2165 - recall: 0.0075 - precision: 0.2308 - AUROC: 0.6260 - AUPRC: 0.1699 - f1_score: 0.0145 - balanced_accuracy: 0.5024 - specificity: 0.9972 - miss_rate: 0.9925 - fall_out: 0.0028 - mcc: 0.0249 - val_loss: 2.1040 - val_accuracy: 0.3450 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7837 - val_AUPRC: 0.3188 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.4997 - val_specificity: 0.9994 - val_miss_rate: 1.0000 - val_fall_out: 5.5556e-04 - val_mcc: -0.0075
Epoch 6/100
7/7 [==============================] - 0s 14ms/step - loss: 2.3513 - accuracy: 0.1927 - recall: 0.0100 - precision: 0.2581 - AUROC: 0.6255 - AUPRC: 0.1599 - f1_score: 0.0193 - balanced_accuracy: 0.5034 - specificity: 0.9968 - miss_rate: 0.9900 - fall_out: 0.0032 - mcc: 0.0329 - val_loss: 2.0711 - val_accuracy: 0.3550 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.8050 - val_AUPRC: 0.3711 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.4997 - val_specificity: 0.9994 - val_miss_rate: 1.0000 - val_fall_out: 5.5556e-04 - val_mcc: -0.0075
Epoch 7/100
7/7 [==============================] - 0s 14ms/step - loss: 2.2394 - accuracy: 0.2265 - recall: 0.0125 - precision: 0.2857 - AUROC: 0.6611 - AUPRC: 0.1913 - f1_score: 0.0240 - balanced_accuracy: 0.5045 - specificity: 0.9965 - miss_rate: 0.9875 - fall_out: 0.0035 - mcc: 0.0411 - val_loss: 2.0344 - val_accuracy: 0.3500 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.8142 - val_AUPRC: 0.3925 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.4997 - val_specificity: 0.9994 - val_miss_rate: 1.0000 - val_fall_out: 5.5556e-04 - val_mcc: -0.0075
Epoch 8/100
7/7 [==============================] - 0s 14ms/step - loss: 2.1730 - accuracy: 0.2591 - recall: 0.0163 - precision: 0.4333 - AUROC: 0.6786 - AUPRC: 0.2153 - f1_score: 0.0314 - balanced_accuracy: 0.5070 - specificity: 0.9976 - miss_rate: 0.9837 - fall_out: 0.0024 - mcc: 0.0682 - val_loss: 1.9879 - val_accuracy: 0.3700 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.8277 - val_AUPRC: 0.4166 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.4997 - val_specificity: 0.9994 - val_miss_rate: 1.0000 - val_fall_out: 5.5556e-04 - val_mcc: -0.0075
Epoch 9/100
7/7 [==============================] - 0s 13ms/step - loss: 2.1159 - accuracy: 0.2766 - recall: 0.0375 - precision: 0.5882 - AUROC: 0.7014 - AUPRC: 0.2467 - f1_score: 0.0706 - balanced_accuracy: 0.5173 - specificity: 0.9971 - miss_rate: 0.9625 - fall_out: 0.0029 - mcc: 0.1304 - val_loss: 1.9359 - val_accuracy: 0.3700 - val_recall: 0.0100 - val_precision: 0.6667 - val_AUROC: 0.8355 - val_AUPRC: 0.4249 - val_f1_score: 0.0197 - val_balanced_accuracy: 0.5047 - val_specificity: 0.9994 - val_miss_rate: 0.9900 - val_fall_out: 5.5556e-04 - val_mcc: 0.0732
Epoch 10/100
7/7 [==============================] - 0s 12ms/step - loss: 2.1196 - accuracy: 0.3129 - recall: 0.0413 - precision: 0.5500 - AUROC: 0.7150 - AUPRC: 0.2590 - f1_score: 0.0768 - balanced_accuracy: 0.5188 - specificity: 0.9962 - miss_rate: 0.9587 - fall_out: 0.0038 - mcc: 0.1305 - val_loss: 1.8842 - val_accuracy: 0.3800 - val_recall: 0.0350 - val_precision: 0.8750 - val_AUROC: 0.8477 - val_AUPRC: 0.4390 - val_f1_score: 0.0673 - val_balanced_accuracy: 0.5172 - val_specificity: 0.9994 - val_miss_rate: 0.9650 - val_fall_out: 5.5556e-04 - val_mcc: 0.1637
Epoch 11/100
7/7 [==============================] - 0s 12ms/step - loss: 2.0654 - accuracy: 0.2879 - recall: 0.0476 - precision: 0.5135 - AUROC: 0.7218 - AUPRC: 0.2507 - f1_score: 0.0871 - balanced_accuracy: 0.5213 - specificity: 0.9950 - miss_rate: 0.9524 - fall_out: 0.0050 - mcc: 0.1333 - val_loss: 1.8095 - val_accuracy: 0.3900 - val_recall: 0.0700 - val_precision: 0.9333 - val_AUROC: 0.8603 - val_AUPRC: 0.4507 - val_f1_score: 0.1302 - val_balanced_accuracy: 0.5347 - val_specificity: 0.9994 - val_miss_rate: 0.9300 - val_fall_out: 5.5556e-04 - val_mcc: 0.2415
Epoch 12/100
7/7 [==============================] - 0s 12ms/step - loss: 2.0372 - accuracy: 0.2879 - recall: 0.0701 - precision: 0.6154 - AUROC: 0.7303 - AUPRC: 0.2790 - f1_score: 0.1258 - balanced_accuracy: 0.5326 - specificity: 0.9951 - miss_rate: 0.9299 - fall_out: 0.0049 - mcc: 0.1844 - val_loss: 1.7643 - val_accuracy: 0.3900 - val_recall: 0.0900 - val_precision: 0.9474 - val_AUROC: 0.8653 - val_AUPRC: 0.4620 - val_f1_score: 0.1644 - val_balanced_accuracy: 0.5447 - val_specificity: 0.9994 - val_miss_rate: 0.9100 - val_fall_out: 5.5556e-04 - val_mcc: 0.2766
Epoch 13/100
7/7 [==============================] - 0s 11ms/step - loss: 1.9784 - accuracy: 0.2854 - recall: 0.0851 - precision: 0.6182 - AUROC: 0.7599 - AUPRC: 0.3061 - f1_score: 0.1496 - balanced_accuracy: 0.5396 - specificity: 0.9942 - miss_rate: 0.9149 - fall_out: 0.0058 - mcc: 0.2041 - val_loss: 1.7180 - val_accuracy: 0.4000 - val_recall: 0.1100 - val_precision: 0.9167 - val_AUROC: 0.8679 - val_AUPRC: 0.4834 - val_f1_score: 0.1964 - val_balanced_accuracy: 0.5544 - val_specificity: 0.9989 - val_miss_rate: 0.8900 - val_fall_out: 0.0011 - val_mcc: 0.3000
Epoch 14/100
7/7 [==============================] - 0s 12ms/step - loss: 1.9450 - accuracy: 0.3304 - recall: 0.0914 - precision: 0.6239 - AUROC: 0.7717 - AUPRC: 0.3284 - f1_score: 0.1594 - balanced_accuracy: 0.5426 - specificity: 0.9939 - miss_rate: 0.9086 - fall_out: 0.0061 - mcc: 0.2129 - val_loss: 1.6746 - val_accuracy: 0.4150 - val_recall: 0.1300 - val_precision: 0.9286 - val_AUROC: 0.8746 - val_AUPRC: 0.4999 - val_f1_score: 0.2281 - val_balanced_accuracy: 0.5644 - val_specificity: 0.9989 - val_miss_rate: 0.8700 - val_fall_out: 0.0011 - val_mcc: 0.3291
Epoch 15/100
7/7 [==============================] - 0s 12ms/step - loss: 1.9552 - accuracy: 0.3379 - recall: 0.1126 - precision: 0.6667 - AUROC: 0.7703 - AUPRC: 0.3329 - f1_score: 0.1927 - balanced_accuracy: 0.5532 - specificity: 0.9937 - miss_rate: 0.8874 - fall_out: 0.0063 - mcc: 0.2476 - val_loss: 1.6498 - val_accuracy: 0.4150 - val_recall: 0.1350 - val_precision: 0.9000 - val_AUROC: 0.8780 - val_AUPRC: 0.4921 - val_f1_score: 0.2348 - val_balanced_accuracy: 0.5667 - val_specificity: 0.9983 - val_miss_rate: 0.8650 - val_fall_out: 0.0017 - val_mcc: 0.3291
Epoch 16/100
7/7 [==============================] - 0s 12ms/step - loss: 1.8865 - accuracy: 0.3442 - recall: 0.1089 - precision: 0.6259 - AUROC: 0.7960 - AUPRC: 0.3451 - f1_score: 0.1855 - balanced_accuracy: 0.5508 - specificity: 0.9928 - miss_rate: 0.8911 - fall_out: 0.0072 - mcc: 0.2333 - val_loss: 1.6148 - val_accuracy: 0.4200 - val_recall: 0.1250 - val_precision: 0.9259 - val_AUROC: 0.8829 - val_AUPRC: 0.5069 - val_f1_score: 0.2203 - val_balanced_accuracy: 0.5619 - val_specificity: 0.9989 - val_miss_rate: 0.8750 - val_fall_out: 0.0011 - val_mcc: 0.3221
Epoch 17/100
7/7 [==============================] - 0s 12ms/step - loss: 1.8632 - accuracy: 0.3467 - recall: 0.1202 - precision: 0.6667 - AUROC: 0.7917 - AUPRC: 0.3601 - f1_score: 0.2036 - balanced_accuracy: 0.5567 - specificity: 0.9933 - miss_rate: 0.8798 - fall_out: 0.0067 - mcc: 0.2559 - val_loss: 1.5835 - val_accuracy: 0.4350 - val_recall: 0.1500 - val_precision: 0.8824 - val_AUROC: 0.8860 - val_AUPRC: 0.5126 - val_f1_score: 0.2564 - val_balanced_accuracy: 0.5739 - val_specificity: 0.9978 - val_miss_rate: 0.8500 - val_fall_out: 0.0022 - val_mcc: 0.3429
Epoch 18/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7394 - accuracy: 0.3717 - recall: 0.1402 - precision: 0.6437 - AUROC: 0.8176 - AUPRC: 0.3945 - f1_score: 0.2302 - balanced_accuracy: 0.5658 - specificity: 0.9914 - miss_rate: 0.8598 - fall_out: 0.0086 - mcc: 0.2704 - val_loss: 1.5522 - val_accuracy: 0.4550 - val_recall: 0.1650 - val_precision: 0.9167 - val_AUROC: 0.8902 - val_AUPRC: 0.5243 - val_f1_score: 0.2797 - val_balanced_accuracy: 0.5817 - val_specificity: 0.9983 - val_miss_rate: 0.8350 - val_fall_out: 0.0017 - val_mcc: 0.3686
Epoch 19/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7546 - accuracy: 0.4055 - recall: 0.1377 - precision: 0.6748 - AUROC: 0.8228 - AUPRC: 0.4009 - f1_score: 0.2287 - balanced_accuracy: 0.5652 - specificity: 0.9926 - miss_rate: 0.8623 - fall_out: 0.0074 - mcc: 0.2765 - val_loss: 1.5231 - val_accuracy: 0.4600 - val_recall: 0.1900 - val_precision: 0.9048 - val_AUROC: 0.8920 - val_AUPRC: 0.5338 - val_f1_score: 0.3140 - val_balanced_accuracy: 0.5939 - val_specificity: 0.9978 - val_miss_rate: 0.8100 - val_fall_out: 0.0022 - val_mcc: 0.3929
Epoch 20/100
7/7 [==============================] - 0s 11ms/step - loss: 1.7461 - accuracy: 0.3742 - recall: 0.1464 - precision: 0.6223 - AUROC: 0.8254 - AUPRC: 0.3971 - f1_score: 0.2371 - balanced_accuracy: 0.5683 - specificity: 0.9901 - miss_rate: 0.8536 - fall_out: 0.0099 - mcc: 0.2703 - val_loss: 1.5026 - val_accuracy: 0.4650 - val_recall: 0.1950 - val_precision: 0.8667 - val_AUROC: 0.8951 - val_AUPRC: 0.5384 - val_f1_score: 0.3184 - val_balanced_accuracy: 0.5958 - val_specificity: 0.9967 - val_miss_rate: 0.8050 - val_fall_out: 0.0033 - val_mcc: 0.3877
Epoch 21/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7424 - accuracy: 0.4030 - recall: 0.1690 - precision: 0.6750 - AUROC: 0.8205 - AUPRC: 0.4156 - f1_score: 0.2703 - balanced_accuracy: 0.5800 - specificity: 0.9910 - miss_rate: 0.8310 - fall_out: 0.0090 - mcc: 0.3071 - val_loss: 1.4795 - val_accuracy: 0.4750 - val_recall: 0.1900 - val_precision: 0.9048 - val_AUROC: 0.8980 - val_AUPRC: 0.5482 - val_f1_score: 0.3140 - val_balanced_accuracy: 0.5939 - val_specificity: 0.9978 - val_miss_rate: 0.8100 - val_fall_out: 0.0022 - val_mcc: 0.3929
Epoch 22/100
7/7 [==============================] - 0s 11ms/step - loss: 1.7193 - accuracy: 0.3867 - recall: 0.1589 - precision: 0.6414 - AUROC: 0.8291 - AUPRC: 0.4009 - f1_score: 0.2548 - balanced_accuracy: 0.5745 - specificity: 0.9901 - miss_rate: 0.8411 - fall_out: 0.0099 - mcc: 0.2877 - val_loss: 1.4616 - val_accuracy: 0.4850 - val_recall: 0.1900 - val_precision: 0.8837 - val_AUROC: 0.8999 - val_AUPRC: 0.5527 - val_f1_score: 0.3128 - val_balanced_accuracy: 0.5936 - val_specificity: 0.9972 - val_miss_rate: 0.8100 - val_fall_out: 0.0028 - val_mcc: 0.3872
Epoch 23/100
7/7 [==============================] - 0s 12ms/step - loss: 1.6600 - accuracy: 0.4043 - recall: 0.1652 - precision: 0.6439 - AUROC: 0.8420 - AUPRC: 0.4230 - f1_score: 0.2629 - balanced_accuracy: 0.5775 - specificity: 0.9898 - miss_rate: 0.8348 - fall_out: 0.0102 - mcc: 0.2942 - val_loss: 1.4383 - val_accuracy: 0.4950 - val_recall: 0.1950 - val_precision: 0.8667 - val_AUROC: 0.9012 - val_AUPRC: 0.5702 - val_f1_score: 0.3184 - val_balanced_accuracy: 0.5958 - val_specificity: 0.9967 - val_miss_rate: 0.8050 - val_fall_out: 0.0033 - val_mcc: 0.3877
Epoch 24/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7019 - accuracy: 0.4030 - recall: 0.1702 - precision: 0.6385 - AUROC: 0.8321 - AUPRC: 0.4212 - f1_score: 0.2688 - balanced_accuracy: 0.5798 - specificity: 0.9893 - miss_rate: 0.8298 - fall_out: 0.0107 - mcc: 0.2971 - val_loss: 1.4215 - val_accuracy: 0.5050 - val_recall: 0.2150 - val_precision: 0.8431 - val_AUROC: 0.9031 - val_AUPRC: 0.5759 - val_f1_score: 0.3426 - val_balanced_accuracy: 0.6053 - val_specificity: 0.9956 - val_miss_rate: 0.7850 - val_fall_out: 0.0044 - val_mcc: 0.4007
Epoch 25/100
7/7 [==============================] - 0s 12ms/step - loss: 1.6429 - accuracy: 0.4093 - recall: 0.2015 - precision: 0.6822 - AUROC: 0.8438 - AUPRC: 0.4395 - f1_score: 0.3111 - balanced_accuracy: 0.5955 - specificity: 0.9896 - miss_rate: 0.7985 - fall_out: 0.0104 - mcc: 0.3386 - val_loss: 1.4045 - val_accuracy: 0.4950 - val_recall: 0.2250 - val_precision: 0.8491 - val_AUROC: 0.9058 - val_AUPRC: 0.5823 - val_f1_score: 0.3557 - val_balanced_accuracy: 0.6103 - val_specificity: 0.9956 - val_miss_rate: 0.7750 - val_fall_out: 0.0044 - val_mcc: 0.4120
Epoch 26/100
7/7 [==============================] - 0s 12ms/step - loss: 1.6600 - accuracy: 0.4080 - recall: 0.1790 - precision: 0.6560 - AUROC: 0.8381 - AUPRC: 0.4393 - f1_score: 0.2812 - balanced_accuracy: 0.5843 - specificity: 0.9896 - miss_rate: 0.8210 - fall_out: 0.0104 - mcc: 0.3104 - val_loss: 1.3888 - val_accuracy: 0.5150 - val_recall: 0.2250 - val_precision: 0.8182 - val_AUROC: 0.9077 - val_AUPRC: 0.5911 - val_f1_score: 0.3529 - val_balanced_accuracy: 0.6097 - val_specificity: 0.9944 - val_miss_rate: 0.7750 - val_fall_out: 0.0056 - val_mcc: 0.4026
Epoch 27/100
7/7 [==============================] - 0s 12ms/step - loss: 1.5694 - accuracy: 0.4406 - recall: 0.2065 - precision: 0.7366 - AUROC: 0.8568 - AUPRC: 0.4664 - f1_score: 0.3226 - balanced_accuracy: 0.5992 - specificity: 0.9918 - miss_rate: 0.7935 - fall_out: 0.0082 - mcc: 0.3604 - val_loss: 1.3699 - val_accuracy: 0.5350 - val_recall: 0.2300 - val_precision: 0.8679 - val_AUROC: 0.9101 - val_AUPRC: 0.5952 - val_f1_score: 0.3636 - val_balanced_accuracy: 0.6131 - val_specificity: 0.9961 - val_miss_rate: 0.7700 - val_fall_out: 0.0039 - val_mcc: 0.4223
Epoch 28/100
7/7 [==============================] - 0s 12ms/step - loss: 1.6075 - accuracy: 0.4055 - recall: 0.1977 - precision: 0.6667 - AUROC: 0.8589 - AUPRC: 0.4564 - f1_score: 0.3050 - balanced_accuracy: 0.5934 - specificity: 0.9890 - miss_rate: 0.8023 - fall_out: 0.0110 - mcc: 0.3303 - val_loss: 1.3586 - val_accuracy: 0.5350 - val_recall: 0.2500 - val_precision: 0.8065 - val_AUROC: 0.9102 - val_AUPRC: 0.5948 - val_f1_score: 0.3817 - val_balanced_accuracy: 0.6217 - val_specificity: 0.9933 - val_miss_rate: 0.7500 - val_fall_out: 0.0067 - val_mcc: 0.4212
Epoch 29/100
7/7 [==============================] - 0s 12ms/step - loss: 1.5413 - accuracy: 0.4556 - recall: 0.2240 - precision: 0.7189 - AUROC: 0.8669 - AUPRC: 0.4974 - f1_score: 0.3416 - balanced_accuracy: 0.6071 - specificity: 0.9903 - miss_rate: 0.7760 - fall_out: 0.0097 - mcc: 0.3700 - val_loss: 1.3307 - val_accuracy: 0.5450 - val_recall: 0.2350 - val_precision: 0.8246 - val_AUROC: 0.9136 - val_AUPRC: 0.6088 - val_f1_score: 0.3658 - val_balanced_accuracy: 0.6147 - val_specificity: 0.9944 - val_miss_rate: 0.7650 - val_fall_out: 0.0056 - val_mcc: 0.4137
Epoch 30/100
7/7 [==============================] - 0s 12ms/step - loss: 1.6057 - accuracy: 0.4255 - recall: 0.1977 - precision: 0.6529 - AUROC: 0.8587 - AUPRC: 0.4611 - f1_score: 0.3036 - balanced_accuracy: 0.5930 - specificity: 0.9883 - miss_rate: 0.8023 - fall_out: 0.0117 - mcc: 0.3257 - val_loss: 1.3188 - val_accuracy: 0.5500 - val_recall: 0.2650 - val_precision: 0.8548 - val_AUROC: 0.9149 - val_AUPRC: 0.6196 - val_f1_score: 0.4046 - val_balanced_accuracy: 0.6300 - val_specificity: 0.9950 - val_miss_rate: 0.7350 - val_fall_out: 0.0050 - val_mcc: 0.4500
Epoch 31/100
7/7 [==============================] - 0s 14ms/step - loss: 1.5103 - accuracy: 0.4856 - recall: 0.2265 - precision: 0.7269 - AUROC: 0.8734 - AUPRC: 0.5032 - f1_score: 0.3454 - balanced_accuracy: 0.6085 - specificity: 0.9905 - miss_rate: 0.7735 - fall_out: 0.0095 - mcc: 0.3748 - val_loss: 1.3022 - val_accuracy: 0.5750 - val_recall: 0.2750 - val_precision: 0.8462 - val_AUROC: 0.9167 - val_AUPRC: 0.6256 - val_f1_score: 0.4151 - val_balanced_accuracy: 0.6347 - val_specificity: 0.9944 - val_miss_rate: 0.7250 - val_fall_out: 0.0056 - val_mcc: 0.4559
Epoch 32/100
7/7 [==============================] - 0s 13ms/step - loss: 1.4711 - accuracy: 0.4581 - recall: 0.2516 - precision: 0.7077 - AUROC: 0.8782 - AUPRC: 0.5089 - f1_score: 0.3712 - balanced_accuracy: 0.6200 - specificity: 0.9885 - miss_rate: 0.7484 - fall_out: 0.0115 - mcc: 0.3889 - val_loss: 1.2855 - val_accuracy: 0.5750 - val_recall: 0.2650 - val_precision: 0.8548 - val_AUROC: 0.9182 - val_AUPRC: 0.6297 - val_f1_score: 0.4046 - val_balanced_accuracy: 0.6300 - val_specificity: 0.9950 - val_miss_rate: 0.7350 - val_fall_out: 0.0050 - val_mcc: 0.4500
Epoch 33/100
7/7 [==============================] - 0s 11ms/step - loss: 1.4968 - accuracy: 0.4606 - recall: 0.2541 - precision: 0.7173 - AUROC: 0.8713 - AUPRC: 0.5020 - f1_score: 0.3752 - balanced_accuracy: 0.6215 - specificity: 0.9889 - miss_rate: 0.7459 - fall_out: 0.0111 - mcc: 0.3943 - val_loss: 1.2723 - val_accuracy: 0.5650 - val_recall: 0.3000 - val_precision: 0.8824 - val_AUROC: 0.9196 - val_AUPRC: 0.6345 - val_f1_score: 0.4478 - val_balanced_accuracy: 0.6478 - val_specificity: 0.9956 - val_miss_rate: 0.7000 - val_fall_out: 0.0044 - val_mcc: 0.4893
Epoch 34/100
7/7 [==============================] - 0s 11ms/step - loss: 1.4732 - accuracy: 0.4468 - recall: 0.2353 - precision: 0.6267 - AUROC: 0.8794 - AUPRC: 0.4968 - f1_score: 0.3421 - balanced_accuracy: 0.6099 - specificity: 0.9844 - miss_rate: 0.7647 - fall_out: 0.0156 - mcc: 0.3467 - val_loss: 1.2623 - val_accuracy: 0.5650 - val_recall: 0.3050 - val_precision: 0.8356 - val_AUROC: 0.9208 - val_AUPRC: 0.6307 - val_f1_score: 0.4469 - val_balanced_accuracy: 0.6492 - val_specificity: 0.9933 - val_miss_rate: 0.6950 - val_fall_out: 0.0067 - val_mcc: 0.4773
Epoch 35/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4752 - accuracy: 0.4956 - recall: 0.2566 - precision: 0.7069 - AUROC: 0.8774 - AUPRC: 0.5232 - f1_score: 0.3765 - balanced_accuracy: 0.6224 - specificity: 0.9882 - miss_rate: 0.7434 - fall_out: 0.0118 - mcc: 0.3926 - val_loss: 1.2545 - val_accuracy: 0.5750 - val_recall: 0.3100 - val_precision: 0.8493 - val_AUROC: 0.9210 - val_AUPRC: 0.6296 - val_f1_score: 0.4542 - val_balanced_accuracy: 0.6519 - val_specificity: 0.9939 - val_miss_rate: 0.6900 - val_fall_out: 0.0061 - val_mcc: 0.4861
Epoch 36/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4381 - accuracy: 0.4756 - recall: 0.2541 - precision: 0.7148 - AUROC: 0.8832 - AUPRC: 0.5361 - f1_score: 0.3749 - balanced_accuracy: 0.6214 - specificity: 0.9887 - miss_rate: 0.7459 - fall_out: 0.0113 - mcc: 0.3934 - val_loss: 1.2342 - val_accuracy: 0.5700 - val_recall: 0.3150 - val_precision: 0.8400 - val_AUROC: 0.9242 - val_AUPRC: 0.6409 - val_f1_score: 0.4582 - val_balanced_accuracy: 0.6542 - val_specificity: 0.9933 - val_miss_rate: 0.6850 - val_fall_out: 0.0067 - val_mcc: 0.4869
Epoch 37/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3799 - accuracy: 0.5106 - recall: 0.2954 - precision: 0.7588 - AUROC: 0.8935 - AUPRC: 0.5601 - f1_score: 0.4252 - balanced_accuracy: 0.6425 - specificity: 0.9896 - miss_rate: 0.7046 - fall_out: 0.0104 - mcc: 0.4420 - val_loss: 1.2117 - val_accuracy: 0.5750 - val_recall: 0.3400 - val_precision: 0.8395 - val_AUROC: 0.9270 - val_AUPRC: 0.6506 - val_f1_score: 0.4840 - val_balanced_accuracy: 0.6664 - val_specificity: 0.9928 - val_miss_rate: 0.6600 - val_fall_out: 0.0072 - val_mcc: 0.5064
Epoch 38/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4284 - accuracy: 0.4869 - recall: 0.2666 - precision: 0.7007 - AUROC: 0.8878 - AUPRC: 0.5415 - f1_score: 0.3862 - balanced_accuracy: 0.6270 - specificity: 0.9873 - miss_rate: 0.7334 - fall_out: 0.0127 - mcc: 0.3982 - val_loss: 1.2032 - val_accuracy: 0.5850 - val_recall: 0.3250 - val_precision: 0.8025 - val_AUROC: 0.9285 - val_AUPRC: 0.6532 - val_f1_score: 0.4626 - val_balanced_accuracy: 0.6581 - val_specificity: 0.9911 - val_miss_rate: 0.6750 - val_fall_out: 0.0089 - val_mcc: 0.4811
Epoch 39/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4496 - accuracy: 0.5119 - recall: 0.2816 - precision: 0.7282 - AUROC: 0.8839 - AUPRC: 0.5387 - f1_score: 0.4061 - balanced_accuracy: 0.6350 - specificity: 0.9883 - miss_rate: 0.7184 - fall_out: 0.0117 - mcc: 0.4200 - val_loss: 1.1947 - val_accuracy: 0.5800 - val_recall: 0.3100 - val_precision: 0.8052 - val_AUROC: 0.9294 - val_AUPRC: 0.6594 - val_f1_score: 0.4477 - val_balanced_accuracy: 0.6508 - val_specificity: 0.9917 - val_miss_rate: 0.6900 - val_fall_out: 0.0083 - val_mcc: 0.4704
Epoch 40/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4219 - accuracy: 0.4768 - recall: 0.2653 - precision: 0.6974 - AUROC: 0.8910 - AUPRC: 0.5381 - f1_score: 0.3844 - balanced_accuracy: 0.6263 - specificity: 0.9872 - miss_rate: 0.7347 - fall_out: 0.0128 - mcc: 0.3960 - val_loss: 1.2106 - val_accuracy: 0.6150 - val_recall: 0.3100 - val_precision: 0.7848 - val_AUROC: 0.9261 - val_AUPRC: 0.6532 - val_f1_score: 0.4444 - val_balanced_accuracy: 0.6503 - val_specificity: 0.9906 - val_miss_rate: 0.6900 - val_fall_out: 0.0094 - val_mcc: 0.4629
Epoch 41/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4479 - accuracy: 0.4919 - recall: 0.2703 - precision: 0.7129 - AUROC: 0.8831 - AUPRC: 0.5260 - f1_score: 0.3920 - balanced_accuracy: 0.6291 - specificity: 0.9879 - miss_rate: 0.7297 - fall_out: 0.0121 - mcc: 0.4056 - val_loss: 1.2149 - val_accuracy: 0.5950 - val_recall: 0.3200 - val_precision: 0.7901 - val_AUROC: 0.9260 - val_AUPRC: 0.6442 - val_f1_score: 0.4555 - val_balanced_accuracy: 0.6553 - val_specificity: 0.9906 - val_miss_rate: 0.6800 - val_fall_out: 0.0094 - val_mcc: 0.4726
25/25 [==============================] - 0s 4ms/step - loss: 1.0729 - accuracy: 0.6458 - recall: 0.3705 - precision: 0.8997 - AUROC: 0.9497 - AUPRC: 0.7377 - f1_score: 0.5248 - balanced_accuracy: 0.6829 - specificity: 0.9954 - miss_rate: 0.6295 - fall_out: 0.0046 - mcc: 0.5524
7/7 [==============================] - 0s 5ms/step - loss: 1.2149 - accuracy: 0.5950 - recall: 0.3200 - precision: 0.7901 - AUROC: 0.9260 - AUPRC: 0.6442 - f1_score: 0.4555 - balanced_accuracy: 0.6553 - specificity: 0.9906 - miss_rate: 0.6800 - fall_out: 0.0094 - mcc: 0.4726
8it [01:06, 7.69s/it]
-- HOLDOUT 9 -- WINDOW window_30s
-- 22 Uncorrelated features: [Pearson+Spearman]
['mfcc16_mean', 'harmony_mean', 'mfcc10_var', 'mfcc4_mean', 'mfcc15_mean', 'mfcc19_mean', 'mfcc8_var', 'mfcc9_var', 'mfcc20_mean', 'mfcc3_mean', 'chroma_stft_var', 'mfcc3_var', 'mfcc1_var', 'mfcc2_var', 'mfcc7_var', 'tempo', 'mfcc13_mean', 'mfcc5_mean', 'mfcc17_mean', 'mfcc14_mean', 'perceptr_mean', 'mfcc5_var']
-- Actually uncorrelated features: [confirmed via MIC]
[]
-- 1 Correlated features: [Pearson]
['spectral_centroid_mean']
Model: "MLP"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_212 (Dense) (None, 128) 7296
dropout_164 (Dropout) (None, 128) 0
dense_213 (Dense) (None, 64) 8256
dropout_165 (Dropout) (None, 64) 0
dense_214 (Dense) (None, 64) 4160
dropout_166 (Dropout) (None, 64) 0
dense_215 (Dense) (None, 10) 650
=================================================================
Total params: 20,362
Trainable params: 20,362
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
7/7 [==============================] - 4s 88ms/step - loss: 2.8959 - accuracy: 0.0889 - recall: 0.0075 - precision: 0.1224 - AUROC: 0.5039 - AUPRC: 0.1003 - f1_score: 0.0142 - balanced_accuracy: 0.5008 - specificity: 0.9940 - miss_rate: 0.9925 - fall_out: 0.0060 - mcc: 0.0059 - val_loss: 2.2942 - val_accuracy: 0.1400 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5609 - val_AUPRC: 0.1187 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 2/100
7/7 [==============================] - 0s 15ms/step - loss: 2.6283 - accuracy: 0.1051 - recall: 0.0038 - precision: 0.0732 - AUROC: 0.5400 - AUPRC: 0.1128 - f1_score: 0.0071 - balanced_accuracy: 0.4992 - specificity: 0.9947 - miss_rate: 0.9962 - fall_out: 0.0053 - mcc: -0.0064 - val_loss: 2.2381 - val_accuracy: 0.2350 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6487 - val_AUPRC: 0.1729 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 3/100
7/7 [==============================] - 0s 15ms/step - loss: 2.5231 - accuracy: 0.1514 - recall: 0.0025 - precision: 0.0714 - AUROC: 0.5719 - AUPRC: 0.1275 - f1_score: 0.0048 - balanced_accuracy: 0.4994 - specificity: 0.9964 - miss_rate: 0.9975 - fall_out: 0.0036 - mcc: -0.0056 - val_loss: 2.2068 - val_accuracy: 0.2600 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6985 - val_AUPRC: 0.2118 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 4/100
7/7 [==============================] - 0s 16ms/step - loss: 2.4749 - accuracy: 0.2040 - recall: 0.0075 - precision: 0.2308 - AUROC: 0.5963 - AUPRC: 0.1491 - f1_score: 0.0145 - balanced_accuracy: 0.5024 - specificity: 0.9972 - miss_rate: 0.9925 - fall_out: 0.0028 - mcc: 0.0249 - val_loss: 2.1817 - val_accuracy: 0.2600 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7202 - val_AUPRC: 0.2330 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 5/100
7/7 [==============================] - 0s 15ms/step - loss: 2.3369 - accuracy: 0.1852 - recall: 0.0050 - precision: 0.1905 - AUROC: 0.6137 - AUPRC: 0.1521 - f1_score: 0.0098 - balanced_accuracy: 0.5013 - specificity: 0.9976 - miss_rate: 0.9950 - fall_out: 0.0024 - mcc: 0.0155 - val_loss: 2.1481 - val_accuracy: 0.3000 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7523 - val_AUPRC: 0.2644 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 6/100
7/7 [==============================] - 0s 14ms/step - loss: 2.3429 - accuracy: 0.2040 - recall: 0.0063 - precision: 0.2000 - AUROC: 0.6291 - AUPRC: 0.1663 - f1_score: 0.0121 - balanced_accuracy: 0.5017 - specificity: 0.9972 - miss_rate: 0.9937 - fall_out: 0.0028 - mcc: 0.0187 - val_loss: 2.1089 - val_accuracy: 0.2900 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7776 - val_AUPRC: 0.2976 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 7/100
7/7 [==============================] - 0s 14ms/step - loss: 2.2668 - accuracy: 0.2190 - recall: 0.0125 - precision: 0.3125 - AUROC: 0.6552 - AUPRC: 0.1842 - f1_score: 0.0241 - balanced_accuracy: 0.5047 - specificity: 0.9969 - miss_rate: 0.9875 - fall_out: 0.0031 - mcc: 0.0449 - val_loss: 2.0666 - val_accuracy: 0.3350 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7952 - val_AUPRC: 0.3270 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 8/100
7/7 [==============================] - 0s 14ms/step - loss: 2.2045 - accuracy: 0.2253 - recall: 0.0275 - precision: 0.5500 - AUROC: 0.6737 - AUPRC: 0.2043 - f1_score: 0.0524 - balanced_accuracy: 0.5125 - specificity: 0.9975 - miss_rate: 0.9725 - fall_out: 0.0025 - mcc: 0.1064 - val_loss: 2.0325 - val_accuracy: 0.3350 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.8058 - val_AUPRC: 0.3408 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 9/100
7/7 [==============================] - 0s 14ms/step - loss: 2.1413 - accuracy: 0.2466 - recall: 0.0375 - precision: 0.5085 - AUROC: 0.6880 - AUPRC: 0.2259 - f1_score: 0.0699 - balanced_accuracy: 0.5168 - specificity: 0.9960 - miss_rate: 0.9625 - fall_out: 0.0040 - mcc: 0.1174 - val_loss: 1.9866 - val_accuracy: 0.3500 - val_recall: 0.0050 - val_precision: 1.0000 - val_AUROC: 0.8130 - val_AUPRC: 0.3609 - val_f1_score: 0.0100 - val_balanced_accuracy: 0.5025 - val_specificity: 1.0000 - val_miss_rate: 0.9950 - val_fall_out: 0.0000e+00 - val_mcc: 0.0671
Epoch 10/100
7/7 [==============================] - 0s 12ms/step - loss: 2.1005 - accuracy: 0.2691 - recall: 0.0463 - precision: 0.5441 - AUROC: 0.7188 - AUPRC: 0.2495 - f1_score: 0.0854 - balanced_accuracy: 0.5210 - specificity: 0.9957 - miss_rate: 0.9537 - fall_out: 0.0043 - mcc: 0.1372 - val_loss: 1.9446 - val_accuracy: 0.3600 - val_recall: 0.0050 - val_precision: 1.0000 - val_AUROC: 0.8194 - val_AUPRC: 0.3802 - val_f1_score: 0.0100 - val_balanced_accuracy: 0.5025 - val_specificity: 1.0000 - val_miss_rate: 0.9950 - val_fall_out: 0.0000e+00 - val_mcc: 0.0671
Epoch 11/100
7/7 [==============================] - 0s 12ms/step - loss: 2.0970 - accuracy: 0.2966 - recall: 0.0438 - precision: 0.5556 - AUROC: 0.7301 - AUPRC: 0.2649 - f1_score: 0.0812 - balanced_accuracy: 0.5200 - specificity: 0.9961 - miss_rate: 0.9562 - fall_out: 0.0039 - mcc: 0.1354 - val_loss: 1.9042 - val_accuracy: 0.3650 - val_recall: 0.0100 - val_precision: 0.6667 - val_AUROC: 0.8270 - val_AUPRC: 0.3967 - val_f1_score: 0.0197 - val_balanced_accuracy: 0.5047 - val_specificity: 0.9994 - val_miss_rate: 0.9900 - val_fall_out: 5.5556e-04 - val_mcc: 0.0732
Epoch 12/100
7/7 [==============================] - 0s 12ms/step - loss: 2.0243 - accuracy: 0.2753 - recall: 0.0626 - precision: 0.5952 - AUROC: 0.7285 - AUPRC: 0.2664 - f1_score: 0.1133 - balanced_accuracy: 0.5289 - specificity: 0.9953 - miss_rate: 0.9374 - fall_out: 0.0047 - mcc: 0.1702 - val_loss: 1.8601 - val_accuracy: 0.3550 - val_recall: 0.0400 - val_precision: 0.8889 - val_AUROC: 0.8352 - val_AUPRC: 0.4146 - val_f1_score: 0.0766 - val_balanced_accuracy: 0.5197 - val_specificity: 0.9994 - val_miss_rate: 0.9600 - val_fall_out: 5.5556e-04 - val_mcc: 0.1768
Epoch 13/100
7/7 [==============================] - 0s 13ms/step - loss: 2.0190 - accuracy: 0.2854 - recall: 0.0801 - precision: 0.5818 - AUROC: 0.7553 - AUPRC: 0.2860 - f1_score: 0.1408 - balanced_accuracy: 0.5369 - specificity: 0.9936 - miss_rate: 0.9199 - fall_out: 0.0064 - mcc: 0.1898 - val_loss: 1.8176 - val_accuracy: 0.3650 - val_recall: 0.0550 - val_precision: 0.9167 - val_AUROC: 0.8438 - val_AUPRC: 0.4291 - val_f1_score: 0.1038 - val_balanced_accuracy: 0.5272 - val_specificity: 0.9994 - val_miss_rate: 0.9450 - val_fall_out: 5.5556e-04 - val_mcc: 0.2115
Epoch 14/100
7/7 [==============================] - 0s 12ms/step - loss: 1.9757 - accuracy: 0.3217 - recall: 0.1001 - precision: 0.6452 - AUROC: 0.7591 - AUPRC: 0.3073 - f1_score: 0.1733 - balanced_accuracy: 0.5470 - specificity: 0.9939 - miss_rate: 0.8999 - fall_out: 0.0061 - mcc: 0.2282 - val_loss: 1.7778 - val_accuracy: 0.3700 - val_recall: 0.0800 - val_precision: 0.9412 - val_AUROC: 0.8510 - val_AUPRC: 0.4413 - val_f1_score: 0.1475 - val_balanced_accuracy: 0.5397 - val_specificity: 0.9994 - val_miss_rate: 0.9200 - val_fall_out: 5.5556e-04 - val_mcc: 0.2596
Epoch 15/100
7/7 [==============================] - 0s 11ms/step - loss: 1.9298 - accuracy: 0.3204 - recall: 0.1101 - precision: 0.6241 - AUROC: 0.7827 - AUPRC: 0.3253 - f1_score: 0.1872 - balanced_accuracy: 0.5514 - specificity: 0.9926 - miss_rate: 0.8899 - fall_out: 0.0074 - mcc: 0.2342 - val_loss: 1.7474 - val_accuracy: 0.3850 - val_recall: 0.1000 - val_precision: 0.9524 - val_AUROC: 0.8565 - val_AUPRC: 0.4541 - val_f1_score: 0.1810 - val_balanced_accuracy: 0.5497 - val_specificity: 0.9994 - val_miss_rate: 0.9000 - val_fall_out: 5.5556e-04 - val_mcc: 0.2927
Epoch 16/100
7/7 [==============================] - 0s 12ms/step - loss: 1.9698 - accuracy: 0.3229 - recall: 0.1114 - precision: 0.5894 - AUROC: 0.7779 - AUPRC: 0.3214 - f1_score: 0.1874 - balanced_accuracy: 0.5514 - specificity: 0.9914 - miss_rate: 0.8886 - fall_out: 0.0086 - mcc: 0.2264 - val_loss: 1.7179 - val_accuracy: 0.3850 - val_recall: 0.1000 - val_precision: 0.9524 - val_AUROC: 0.8621 - val_AUPRC: 0.4649 - val_f1_score: 0.1810 - val_balanced_accuracy: 0.5497 - val_specificity: 0.9994 - val_miss_rate: 0.9000 - val_fall_out: 5.5556e-04 - val_mcc: 0.2927
Epoch 17/100
7/7 [==============================] - 0s 12ms/step - loss: 1.8978 - accuracy: 0.3154 - recall: 0.1014 - precision: 0.6279 - AUROC: 0.7738 - AUPRC: 0.3235 - f1_score: 0.1746 - balanced_accuracy: 0.5474 - specificity: 0.9933 - miss_rate: 0.8986 - fall_out: 0.0067 - mcc: 0.2254 - val_loss: 1.6970 - val_accuracy: 0.3950 - val_recall: 0.1050 - val_precision: 0.9130 - val_AUROC: 0.8632 - val_AUPRC: 0.4701 - val_f1_score: 0.1883 - val_balanced_accuracy: 0.5519 - val_specificity: 0.9989 - val_miss_rate: 0.8950 - val_fall_out: 0.0011 - val_mcc: 0.2923
Epoch 18/100
7/7 [==============================] - 0s 12ms/step - loss: 1.8128 - accuracy: 0.3529 - recall: 0.1189 - precision: 0.6090 - AUROC: 0.8111 - AUPRC: 0.3618 - f1_score: 0.1990 - balanced_accuracy: 0.5552 - specificity: 0.9915 - miss_rate: 0.8811 - fall_out: 0.0085 - mcc: 0.2394 - val_loss: 1.6685 - val_accuracy: 0.3900 - val_recall: 0.1200 - val_precision: 0.8000 - val_AUROC: 0.8671 - val_AUPRC: 0.4800 - val_f1_score: 0.2087 - val_balanced_accuracy: 0.5583 - val_specificity: 0.9967 - val_miss_rate: 0.8800 - val_fall_out: 0.0033 - val_mcc: 0.2879
Epoch 19/100
7/7 [==============================] - 0s 12ms/step - loss: 1.8529 - accuracy: 0.3504 - recall: 0.1364 - precision: 0.6646 - AUROC: 0.7917 - AUPRC: 0.3633 - f1_score: 0.2264 - balanced_accuracy: 0.5644 - specificity: 0.9924 - miss_rate: 0.8636 - fall_out: 0.0076 - mcc: 0.2725 - val_loss: 1.6400 - val_accuracy: 0.4150 - val_recall: 0.1350 - val_precision: 0.8438 - val_AUROC: 0.8696 - val_AUPRC: 0.4903 - val_f1_score: 0.2328 - val_balanced_accuracy: 0.5661 - val_specificity: 0.9972 - val_miss_rate: 0.8650 - val_fall_out: 0.0028 - val_mcc: 0.3161
Epoch 20/100
7/7 [==============================] - 0s 11ms/step - loss: 1.7969 - accuracy: 0.3542 - recall: 0.1477 - precision: 0.6629 - AUROC: 0.8063 - AUPRC: 0.3890 - f1_score: 0.2416 - balanced_accuracy: 0.5697 - specificity: 0.9917 - miss_rate: 0.8523 - fall_out: 0.0083 - mcc: 0.2832 - val_loss: 1.6208 - val_accuracy: 0.4250 - val_recall: 0.1250 - val_precision: 0.8621 - val_AUROC: 0.8734 - val_AUPRC: 0.4972 - val_f1_score: 0.2183 - val_balanced_accuracy: 0.5614 - val_specificity: 0.9978 - val_miss_rate: 0.8750 - val_fall_out: 0.0022 - val_mcc: 0.3081
Epoch 21/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7466 - accuracy: 0.3805 - recall: 0.1539 - precision: 0.6613 - AUROC: 0.8219 - AUPRC: 0.3971 - f1_score: 0.2497 - balanced_accuracy: 0.5726 - specificity: 0.9912 - miss_rate: 0.8461 - fall_out: 0.0088 - mcc: 0.2888 - val_loss: 1.5998 - val_accuracy: 0.4350 - val_recall: 0.1350 - val_precision: 0.8438 - val_AUROC: 0.8784 - val_AUPRC: 0.5054 - val_f1_score: 0.2328 - val_balanced_accuracy: 0.5661 - val_specificity: 0.9972 - val_miss_rate: 0.8650 - val_fall_out: 0.0028 - val_mcc: 0.3161
Epoch 22/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7632 - accuracy: 0.3817 - recall: 0.1477 - precision: 0.6178 - AUROC: 0.8268 - AUPRC: 0.3889 - f1_score: 0.2384 - balanced_accuracy: 0.5688 - specificity: 0.9898 - miss_rate: 0.8523 - fall_out: 0.0102 - mcc: 0.2701 - val_loss: 1.5769 - val_accuracy: 0.4550 - val_recall: 0.1450 - val_precision: 0.8529 - val_AUROC: 0.8818 - val_AUPRC: 0.5143 - val_f1_score: 0.2479 - val_balanced_accuracy: 0.5711 - val_specificity: 0.9972 - val_miss_rate: 0.8550 - val_fall_out: 0.0028 - val_mcc: 0.3301
Epoch 23/100
7/7 [==============================] - 0s 11ms/step - loss: 1.7198 - accuracy: 0.3867 - recall: 0.1589 - precision: 0.6940 - AUROC: 0.8238 - AUPRC: 0.3998 - f1_score: 0.2587 - balanced_accuracy: 0.5756 - specificity: 0.9922 - miss_rate: 0.8411 - fall_out: 0.0078 - mcc: 0.3031 - val_loss: 1.5488 - val_accuracy: 0.4650 - val_recall: 0.1550 - val_precision: 0.8378 - val_AUROC: 0.8859 - val_AUPRC: 0.5249 - val_f1_score: 0.2616 - val_balanced_accuracy: 0.5758 - val_specificity: 0.9967 - val_miss_rate: 0.8450 - val_fall_out: 0.0033 - val_mcc: 0.3377
Epoch 24/100
7/7 [==============================] - 0s 11ms/step - loss: 1.7419 - accuracy: 0.3767 - recall: 0.1552 - precision: 0.6492 - AUROC: 0.8274 - AUPRC: 0.3984 - f1_score: 0.2505 - balanced_accuracy: 0.5729 - specificity: 0.9907 - miss_rate: 0.8448 - fall_out: 0.0093 - mcc: 0.2865 - val_loss: 1.5215 - val_accuracy: 0.4900 - val_recall: 0.1750 - val_precision: 0.8537 - val_AUROC: 0.8896 - val_AUPRC: 0.5360 - val_f1_score: 0.2905 - val_balanced_accuracy: 0.5858 - val_specificity: 0.9967 - val_miss_rate: 0.8250 - val_fall_out: 0.0033 - val_mcc: 0.3634
Epoch 25/100
7/7 [==============================] - 0s 12ms/step - loss: 1.6996 - accuracy: 0.3805 - recall: 0.1665 - precision: 0.6364 - AUROC: 0.8329 - AUPRC: 0.4024 - f1_score: 0.2639 - balanced_accuracy: 0.5779 - specificity: 0.9894 - miss_rate: 0.8335 - fall_out: 0.0106 - mcc: 0.2930 - val_loss: 1.4967 - val_accuracy: 0.4800 - val_recall: 0.1700 - val_precision: 0.8500 - val_AUROC: 0.8952 - val_AUPRC: 0.5477 - val_f1_score: 0.2833 - val_balanced_accuracy: 0.5833 - val_specificity: 0.9967 - val_miss_rate: 0.8300 - val_fall_out: 0.0033 - val_mcc: 0.3571
Epoch 26/100
7/7 [==============================] - 0s 12ms/step - loss: 1.6681 - accuracy: 0.4243 - recall: 0.1727 - precision: 0.6571 - AUROC: 0.8473 - AUPRC: 0.4307 - f1_score: 0.2735 - balanced_accuracy: 0.5814 - specificity: 0.9900 - miss_rate: 0.8273 - fall_out: 0.0100 - mcc: 0.3051 - val_loss: 1.4784 - val_accuracy: 0.4900 - val_recall: 0.1650 - val_precision: 0.8462 - val_AUROC: 0.8979 - val_AUPRC: 0.5540 - val_f1_score: 0.2762 - val_balanced_accuracy: 0.5808 - val_specificity: 0.9967 - val_miss_rate: 0.8350 - val_fall_out: 0.0033 - val_mcc: 0.3508
Epoch 27/100
7/7 [==============================] - 0s 12ms/step - loss: 1.6469 - accuracy: 0.3792 - recall: 0.1677 - precision: 0.6233 - AUROC: 0.8439 - AUPRC: 0.4271 - f1_score: 0.2643 - balanced_accuracy: 0.5782 - specificity: 0.9887 - miss_rate: 0.8323 - fall_out: 0.0113 - mcc: 0.2900 - val_loss: 1.4581 - val_accuracy: 0.5000 - val_recall: 0.1650 - val_precision: 0.9167 - val_AUROC: 0.9006 - val_AUPRC: 0.5644 - val_f1_score: 0.2797 - val_balanced_accuracy: 0.5817 - val_specificity: 0.9983 - val_miss_rate: 0.8350 - val_fall_out: 0.0017 - val_mcc: 0.3686
Epoch 28/100
7/7 [==============================] - 0s 12ms/step - loss: 1.6260 - accuracy: 0.4368 - recall: 0.1827 - precision: 0.6952 - AUROC: 0.8503 - AUPRC: 0.4530 - f1_score: 0.2894 - balanced_accuracy: 0.5869 - specificity: 0.9911 - miss_rate: 0.8173 - fall_out: 0.0089 - mcc: 0.3260 - val_loss: 1.4423 - val_accuracy: 0.4800 - val_recall: 0.1950 - val_precision: 0.8298 - val_AUROC: 0.9014 - val_AUPRC: 0.5630 - val_f1_score: 0.3158 - val_balanced_accuracy: 0.5953 - val_specificity: 0.9956 - val_miss_rate: 0.8050 - val_fall_out: 0.0044 - val_mcc: 0.3774
Epoch 29/100
7/7 [==============================] - 0s 12ms/step - loss: 1.6426 - accuracy: 0.4305 - recall: 0.1965 - precision: 0.6515 - AUROC: 0.8511 - AUPRC: 0.4352 - f1_score: 0.3019 - balanced_accuracy: 0.5924 - specificity: 0.9883 - miss_rate: 0.8035 - fall_out: 0.0117 - mcc: 0.3242 - val_loss: 1.4206 - val_accuracy: 0.5200 - val_recall: 0.2100 - val_precision: 0.8571 - val_AUROC: 0.9041 - val_AUPRC: 0.5772 - val_f1_score: 0.3373 - val_balanced_accuracy: 0.6031 - val_specificity: 0.9961 - val_miss_rate: 0.7900 - val_fall_out: 0.0039 - val_mcc: 0.4000
Epoch 30/100
7/7 [==============================] - 0s 12ms/step - loss: 1.5718 - accuracy: 0.4518 - recall: 0.2003 - precision: 0.6612 - AUROC: 0.8627 - AUPRC: 0.4655 - f1_score: 0.3074 - balanced_accuracy: 0.5944 - specificity: 0.9886 - miss_rate: 0.7997 - fall_out: 0.0114 - mcc: 0.3306 - val_loss: 1.4060 - val_accuracy: 0.5400 - val_recall: 0.2150 - val_precision: 0.9149 - val_AUROC: 0.9047 - val_AUPRC: 0.5863 - val_f1_score: 0.3482 - val_balanced_accuracy: 0.6064 - val_specificity: 0.9978 - val_miss_rate: 0.7850 - val_fall_out: 0.0022 - val_mcc: 0.4214
Epoch 31/100
7/7 [==============================] - 0s 14ms/step - loss: 1.5923 - accuracy: 0.4355 - recall: 0.2028 - precision: 0.6778 - AUROC: 0.8564 - AUPRC: 0.4551 - f1_score: 0.3121 - balanced_accuracy: 0.5960 - specificity: 0.9893 - miss_rate: 0.7972 - fall_out: 0.0107 - mcc: 0.3382 - val_loss: 1.3924 - val_accuracy: 0.5300 - val_recall: 0.2200 - val_precision: 0.8000 - val_AUROC: 0.9060 - val_AUPRC: 0.5828 - val_f1_score: 0.3451 - val_balanced_accuracy: 0.6069 - val_specificity: 0.9939 - val_miss_rate: 0.7800 - val_fall_out: 0.0061 - val_mcc: 0.3924
Epoch 32/100
7/7 [==============================] - 0s 13ms/step - loss: 1.4853 - accuracy: 0.4568 - recall: 0.2190 - precision: 0.7143 - AUROC: 0.8775 - AUPRC: 0.5008 - f1_score: 0.3352 - balanced_accuracy: 0.6046 - specificity: 0.9903 - miss_rate: 0.7810 - fall_out: 0.0097 - mcc: 0.3642 - val_loss: 1.3640 - val_accuracy: 0.5550 - val_recall: 0.2200 - val_precision: 0.8302 - val_AUROC: 0.9087 - val_AUPRC: 0.5954 - val_f1_score: 0.3478 - val_balanced_accuracy: 0.6075 - val_specificity: 0.9950 - val_miss_rate: 0.7800 - val_fall_out: 0.0050 - val_mcc: 0.4016
Epoch 33/100
7/7 [==============================] - 0s 12ms/step - loss: 1.5869 - accuracy: 0.4330 - recall: 0.2040 - precision: 0.6494 - AUROC: 0.8652 - AUPRC: 0.4630 - f1_score: 0.3105 - balanced_accuracy: 0.5959 - specificity: 0.9878 - miss_rate: 0.7960 - fall_out: 0.0122 - mcc: 0.3298 - val_loss: 1.3513 - val_accuracy: 0.5450 - val_recall: 0.2350 - val_precision: 0.8103 - val_AUROC: 0.9099 - val_AUPRC: 0.6008 - val_f1_score: 0.3643 - val_balanced_accuracy: 0.6144 - val_specificity: 0.9939 - val_miss_rate: 0.7650 - val_fall_out: 0.0061 - val_mcc: 0.4092
Epoch 34/100
7/7 [==============================] - 0s 12ms/step - loss: 1.5265 - accuracy: 0.4456 - recall: 0.2340 - precision: 0.6608 - AUROC: 0.8725 - AUPRC: 0.4837 - f1_score: 0.3457 - balanced_accuracy: 0.6103 - specificity: 0.9866 - miss_rate: 0.7660 - fall_out: 0.0134 - mcc: 0.3582 - val_loss: 1.3406 - val_accuracy: 0.5500 - val_recall: 0.2500 - val_precision: 0.8333 - val_AUROC: 0.9108 - val_AUPRC: 0.6071 - val_f1_score: 0.3846 - val_balanced_accuracy: 0.6222 - val_specificity: 0.9944 - val_miss_rate: 0.7500 - val_fall_out: 0.0056 - val_mcc: 0.4299
Epoch 35/100
7/7 [==============================] - 0s 12ms/step - loss: 1.5481 - accuracy: 0.4768 - recall: 0.2165 - precision: 0.6337 - AUROC: 0.8653 - AUPRC: 0.4698 - f1_score: 0.3228 - balanced_accuracy: 0.6013 - specificity: 0.9861 - miss_rate: 0.7835 - fall_out: 0.0139 - mcc: 0.3346 - val_loss: 1.3297 - val_accuracy: 0.5700 - val_recall: 0.2400 - val_precision: 0.9057 - val_AUROC: 0.9128 - val_AUPRC: 0.6190 - val_f1_score: 0.3794 - val_balanced_accuracy: 0.6186 - val_specificity: 0.9972 - val_miss_rate: 0.7600 - val_fall_out: 0.0028 - val_mcc: 0.4431
Epoch 36/100
7/7 [==============================] - 0s 12ms/step - loss: 1.5264 - accuracy: 0.4543 - recall: 0.2203 - precision: 0.6617 - AUROC: 0.8770 - AUPRC: 0.4976 - f1_score: 0.3305 - balanced_accuracy: 0.6039 - specificity: 0.9875 - miss_rate: 0.7797 - fall_out: 0.0125 - mcc: 0.3474 - val_loss: 1.3126 - val_accuracy: 0.5450 - val_recall: 0.2450 - val_precision: 0.8305 - val_AUROC: 0.9145 - val_AUPRC: 0.6189 - val_f1_score: 0.3784 - val_balanced_accuracy: 0.6197 - val_specificity: 0.9944 - val_miss_rate: 0.7550 - val_fall_out: 0.0056 - val_mcc: 0.4245
Epoch 37/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4922 - accuracy: 0.4731 - recall: 0.2478 - precision: 0.7046 - AUROC: 0.8783 - AUPRC: 0.5043 - f1_score: 0.3667 - balanced_accuracy: 0.6181 - specificity: 0.9885 - miss_rate: 0.7522 - fall_out: 0.0115 - mcc: 0.3848 - val_loss: 1.3087 - val_accuracy: 0.5450 - val_recall: 0.2550 - val_precision: 0.8361 - val_AUROC: 0.9154 - val_AUPRC: 0.6147 - val_f1_score: 0.3908 - val_balanced_accuracy: 0.6247 - val_specificity: 0.9944 - val_miss_rate: 0.7450 - val_fall_out: 0.0056 - val_mcc: 0.4352
Epoch 38/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4657 - accuracy: 0.4844 - recall: 0.2428 - precision: 0.7080 - AUROC: 0.8793 - AUPRC: 0.5032 - f1_score: 0.3616 - balanced_accuracy: 0.6158 - specificity: 0.9889 - miss_rate: 0.7572 - fall_out: 0.0111 - mcc: 0.3819 - val_loss: 1.2922 - val_accuracy: 0.5600 - val_recall: 0.2750 - val_precision: 0.7971 - val_AUROC: 0.9171 - val_AUPRC: 0.6217 - val_f1_score: 0.4089 - val_balanced_accuracy: 0.6336 - val_specificity: 0.9922 - val_miss_rate: 0.7250 - val_fall_out: 0.0078 - val_mcc: 0.4392
Epoch 39/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3969 - accuracy: 0.5081 - recall: 0.2716 - precision: 0.7092 - AUROC: 0.8919 - AUPRC: 0.5505 - f1_score: 0.3928 - balanced_accuracy: 0.6296 - specificity: 0.9876 - miss_rate: 0.7284 - fall_out: 0.0124 - mcc: 0.4052 - val_loss: 1.2764 - val_accuracy: 0.5700 - val_recall: 0.2900 - val_precision: 0.8529 - val_AUROC: 0.9206 - val_AUPRC: 0.6299 - val_f1_score: 0.4328 - val_balanced_accuracy: 0.6422 - val_specificity: 0.9944 - val_miss_rate: 0.7100 - val_fall_out: 0.0056 - val_mcc: 0.4709
Epoch 40/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4274 - accuracy: 0.4906 - recall: 0.2591 - precision: 0.6923 - AUROC: 0.8878 - AUPRC: 0.5354 - f1_score: 0.3770 - balanced_accuracy: 0.6231 - specificity: 0.9872 - miss_rate: 0.7409 - fall_out: 0.0128 - mcc: 0.3893 - val_loss: 1.2624 - val_accuracy: 0.5550 - val_recall: 0.2850 - val_precision: 0.8507 - val_AUROC: 0.9224 - val_AUPRC: 0.6319 - val_f1_score: 0.4270 - val_balanced_accuracy: 0.6397 - val_specificity: 0.9944 - val_miss_rate: 0.7150 - val_fall_out: 0.0056 - val_mcc: 0.4659
Epoch 41/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4804 - accuracy: 0.4756 - recall: 0.2653 - precision: 0.7043 - AUROC: 0.8798 - AUPRC: 0.5200 - f1_score: 0.3855 - balanced_accuracy: 0.6265 - specificity: 0.9876 - miss_rate: 0.7347 - fall_out: 0.0124 - mcc: 0.3986 - val_loss: 1.2510 - val_accuracy: 0.5500 - val_recall: 0.2850 - val_precision: 0.8507 - val_AUROC: 0.9251 - val_AUPRC: 0.6370 - val_f1_score: 0.4270 - val_balanced_accuracy: 0.6397 - val_specificity: 0.9944 - val_miss_rate: 0.7150 - val_fall_out: 0.0056 - val_mcc: 0.4659
Epoch 42/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4026 - accuracy: 0.5081 - recall: 0.2778 - precision: 0.7003 - AUROC: 0.8924 - AUPRC: 0.5412 - f1_score: 0.3978 - balanced_accuracy: 0.6323 - specificity: 0.9868 - miss_rate: 0.7222 - fall_out: 0.0132 - mcc: 0.4067 - val_loss: 1.2506 - val_accuracy: 0.5600 - val_recall: 0.2850 - val_precision: 0.8906 - val_AUROC: 0.9248 - val_AUPRC: 0.6383 - val_f1_score: 0.4318 - val_balanced_accuracy: 0.6406 - val_specificity: 0.9961 - val_miss_rate: 0.7150 - val_fall_out: 0.0039 - val_mcc: 0.4792
Epoch 43/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3926 - accuracy: 0.4956 - recall: 0.2891 - precision: 0.7380 - AUROC: 0.8902 - AUPRC: 0.5528 - f1_score: 0.4155 - balanced_accuracy: 0.6389 - specificity: 0.9886 - miss_rate: 0.7109 - fall_out: 0.0114 - mcc: 0.4294 - val_loss: 1.2480 - val_accuracy: 0.5700 - val_recall: 0.2950 - val_precision: 0.8551 - val_AUROC: 0.9242 - val_AUPRC: 0.6431 - val_f1_score: 0.4387 - val_balanced_accuracy: 0.6447 - val_specificity: 0.9944 - val_miss_rate: 0.7050 - val_fall_out: 0.0056 - val_mcc: 0.4758
Epoch 44/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3741 - accuracy: 0.5144 - recall: 0.2829 - precision: 0.7129 - AUROC: 0.8984 - AUPRC: 0.5500 - f1_score: 0.4050 - balanced_accuracy: 0.6351 - specificity: 0.9873 - miss_rate: 0.7171 - fall_out: 0.0127 - mcc: 0.4153 - val_loss: 1.2353 - val_accuracy: 0.5950 - val_recall: 0.2950 - val_precision: 0.8676 - val_AUROC: 0.9244 - val_AUPRC: 0.6468 - val_f1_score: 0.4403 - val_balanced_accuracy: 0.6450 - val_specificity: 0.9950 - val_miss_rate: 0.7050 - val_fall_out: 0.0050 - val_mcc: 0.4801
Epoch 45/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3667 - accuracy: 0.5407 - recall: 0.3016 - precision: 0.7415 - AUROC: 0.9007 - AUPRC: 0.5879 - f1_score: 0.4288 - balanced_accuracy: 0.6450 - specificity: 0.9883 - miss_rate: 0.6984 - fall_out: 0.0117 - mcc: 0.4403 - val_loss: 1.2187 - val_accuracy: 0.6000 - val_recall: 0.3100 - val_precision: 0.8611 - val_AUROC: 0.9265 - val_AUPRC: 0.6538 - val_f1_score: 0.4559 - val_balanced_accuracy: 0.6522 - val_specificity: 0.9944 - val_miss_rate: 0.6900 - val_fall_out: 0.0056 - val_mcc: 0.4903
Epoch 46/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3299 - accuracy: 0.5244 - recall: 0.3016 - precision: 0.7370 - AUROC: 0.9016 - AUPRC: 0.5724 - f1_score: 0.4281 - balanced_accuracy: 0.6448 - specificity: 0.9880 - miss_rate: 0.6984 - fall_out: 0.0120 - mcc: 0.4386 - val_loss: 1.2050 - val_accuracy: 0.5950 - val_recall: 0.3350 - val_precision: 0.8590 - val_AUROC: 0.9278 - val_AUPRC: 0.6613 - val_f1_score: 0.4820 - val_balanced_accuracy: 0.6644 - val_specificity: 0.9939 - val_miss_rate: 0.6650 - val_fall_out: 0.0061 - val_mcc: 0.5097
Epoch 47/100
7/7 [==============================] - 0s 11ms/step - loss: 1.3378 - accuracy: 0.5257 - recall: 0.2879 - precision: 0.7165 - AUROC: 0.9028 - AUPRC: 0.5705 - f1_score: 0.4107 - balanced_accuracy: 0.6376 - specificity: 0.9873 - miss_rate: 0.7121 - fall_out: 0.0127 - mcc: 0.4204 - val_loss: 1.1889 - val_accuracy: 0.6000 - val_recall: 0.3450 - val_precision: 0.8625 - val_AUROC: 0.9298 - val_AUPRC: 0.6689 - val_f1_score: 0.4929 - val_balanced_accuracy: 0.6694 - val_specificity: 0.9939 - val_miss_rate: 0.6550 - val_fall_out: 0.0061 - val_mcc: 0.5188
Epoch 48/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3253 - accuracy: 0.5432 - recall: 0.2991 - precision: 0.7156 - AUROC: 0.9041 - AUPRC: 0.5747 - f1_score: 0.4219 - balanced_accuracy: 0.6430 - specificity: 0.9868 - miss_rate: 0.7009 - fall_out: 0.0132 - mcc: 0.4286 - val_loss: 1.1750 - val_accuracy: 0.5950 - val_recall: 0.3700 - val_precision: 0.8605 - val_AUROC: 0.9320 - val_AUPRC: 0.6740 - val_f1_score: 0.5175 - val_balanced_accuracy: 0.6817 - val_specificity: 0.9933 - val_miss_rate: 0.6300 - val_fall_out: 0.0067 - val_mcc: 0.5373
Epoch 49/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3258 - accuracy: 0.5282 - recall: 0.3392 - precision: 0.7344 - AUROC: 0.9053 - AUPRC: 0.5854 - f1_score: 0.4640 - balanced_accuracy: 0.6628 - specificity: 0.9864 - miss_rate: 0.6608 - fall_out: 0.0136 - mcc: 0.4653 - val_loss: 1.1605 - val_accuracy: 0.6050 - val_recall: 0.3550 - val_precision: 0.8875 - val_AUROC: 0.9342 - val_AUPRC: 0.6817 - val_f1_score: 0.5071 - val_balanced_accuracy: 0.6750 - val_specificity: 0.9950 - val_miss_rate: 0.6450 - val_fall_out: 0.0050 - val_mcc: 0.5358
Epoch 50/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3219 - accuracy: 0.5407 - recall: 0.3191 - precision: 0.7370 - AUROC: 0.9019 - AUPRC: 0.5857 - f1_score: 0.4454 - balanced_accuracy: 0.6532 - specificity: 0.9873 - miss_rate: 0.6809 - fall_out: 0.0127 - mcc: 0.4517 - val_loss: 1.1568 - val_accuracy: 0.6000 - val_recall: 0.3550 - val_precision: 0.8452 - val_AUROC: 0.9338 - val_AUPRC: 0.6809 - val_f1_score: 0.5000 - val_balanced_accuracy: 0.6739 - val_specificity: 0.9928 - val_miss_rate: 0.6450 - val_fall_out: 0.0072 - val_mcc: 0.5201
Epoch 51/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2999 - accuracy: 0.5607 - recall: 0.3091 - precision: 0.7508 - AUROC: 0.9086 - AUPRC: 0.5933 - f1_score: 0.4379 - balanced_accuracy: 0.6489 - specificity: 0.9886 - miss_rate: 0.6909 - fall_out: 0.0114 - mcc: 0.4495 - val_loss: 1.1454 - val_accuracy: 0.6150 - val_recall: 0.3450 - val_precision: 0.8625 - val_AUROC: 0.9348 - val_AUPRC: 0.6846 - val_f1_score: 0.4929 - val_balanced_accuracy: 0.6694 - val_specificity: 0.9939 - val_miss_rate: 0.6550 - val_fall_out: 0.0061 - val_mcc: 0.5188
Epoch 52/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2905 - accuracy: 0.5469 - recall: 0.3204 - precision: 0.7072 - AUROC: 0.9094 - AUPRC: 0.5844 - f1_score: 0.4410 - balanced_accuracy: 0.6528 - specificity: 0.9853 - miss_rate: 0.6796 - fall_out: 0.0147 - mcc: 0.4409 - val_loss: 1.1367 - val_accuracy: 0.6000 - val_recall: 0.3700 - val_precision: 0.8506 - val_AUROC: 0.9350 - val_AUPRC: 0.6877 - val_f1_score: 0.5157 - val_balanced_accuracy: 0.6814 - val_specificity: 0.9928 - val_miss_rate: 0.6300 - val_fall_out: 0.0072 - val_mcc: 0.5335
Epoch 53/100
7/7 [==============================] - 0s 13ms/step - loss: 1.2390 - accuracy: 0.5645 - recall: 0.3479 - precision: 0.7374 - AUROC: 0.9145 - AUPRC: 0.6174 - f1_score: 0.4728 - balanced_accuracy: 0.6671 - specificity: 0.9862 - miss_rate: 0.6521 - fall_out: 0.0138 - mcc: 0.4728 - val_loss: 1.1228 - val_accuracy: 0.6000 - val_recall: 0.3750 - val_precision: 0.8721 - val_AUROC: 0.9377 - val_AUPRC: 0.6958 - val_f1_score: 0.5245 - val_balanced_accuracy: 0.6844 - val_specificity: 0.9939 - val_miss_rate: 0.6250 - val_fall_out: 0.0061 - val_mcc: 0.5455
Epoch 54/100
7/7 [==============================] - 0s 15ms/step - loss: 1.2338 - accuracy: 0.5757 - recall: 0.3517 - precision: 0.7375 - AUROC: 0.9178 - AUPRC: 0.6214 - f1_score: 0.4763 - balanced_accuracy: 0.6689 - specificity: 0.9861 - miss_rate: 0.6483 - fall_out: 0.0139 - mcc: 0.4755 - val_loss: 1.1107 - val_accuracy: 0.6000 - val_recall: 0.3850 - val_precision: 0.8750 - val_AUROC: 0.9384 - val_AUPRC: 0.6972 - val_f1_score: 0.5347 - val_balanced_accuracy: 0.6894 - val_specificity: 0.9939 - val_miss_rate: 0.6150 - val_fall_out: 0.0061 - val_mcc: 0.5542
Epoch 55/100
7/7 [==============================] - 0s 14ms/step - loss: 1.3146 - accuracy: 0.5232 - recall: 0.3417 - precision: 0.7339 - AUROC: 0.9056 - AUPRC: 0.5987 - f1_score: 0.4663 - balanced_accuracy: 0.6640 - specificity: 0.9862 - miss_rate: 0.6583 - fall_out: 0.0138 - mcc: 0.4669 - val_loss: 1.1005 - val_accuracy: 0.6250 - val_recall: 0.3900 - val_precision: 0.8966 - val_AUROC: 0.9398 - val_AUPRC: 0.7067 - val_f1_score: 0.5436 - val_balanced_accuracy: 0.6925 - val_specificity: 0.9950 - val_miss_rate: 0.6100 - val_fall_out: 0.0050 - val_mcc: 0.5662
Epoch 56/100
7/7 [==============================] - 0s 13ms/step - loss: 1.2326 - accuracy: 0.5657 - recall: 0.3567 - precision: 0.7461 - AUROC: 0.9154 - AUPRC: 0.6108 - f1_score: 0.4826 - balanced_accuracy: 0.6716 - specificity: 0.9865 - miss_rate: 0.6433 - fall_out: 0.0135 - mcc: 0.4826 - val_loss: 1.0902 - val_accuracy: 0.6350 - val_recall: 0.4050 - val_precision: 0.8901 - val_AUROC: 0.9408 - val_AUPRC: 0.7139 - val_f1_score: 0.5567 - val_balanced_accuracy: 0.6997 - val_specificity: 0.9944 - val_miss_rate: 0.5950 - val_fall_out: 0.0056 - val_mcc: 0.5750
Epoch 57/100
7/7 [==============================] - 0s 13ms/step - loss: 1.2292 - accuracy: 0.5795 - recall: 0.3642 - precision: 0.7405 - AUROC: 0.9173 - AUPRC: 0.6228 - f1_score: 0.4883 - balanced_accuracy: 0.6750 - specificity: 0.9858 - miss_rate: 0.6358 - fall_out: 0.0142 - mcc: 0.4856 - val_loss: 1.0891 - val_accuracy: 0.6200 - val_recall: 0.4200 - val_precision: 0.8842 - val_AUROC: 0.9396 - val_AUPRC: 0.7157 - val_f1_score: 0.5695 - val_balanced_accuracy: 0.7069 - val_specificity: 0.9939 - val_miss_rate: 0.5800 - val_fall_out: 0.0061 - val_mcc: 0.5837
Epoch 58/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2365 - accuracy: 0.5720 - recall: 0.3417 - precision: 0.7562 - AUROC: 0.9165 - AUPRC: 0.6228 - f1_score: 0.4707 - balanced_accuracy: 0.6647 - specificity: 0.9878 - miss_rate: 0.6583 - fall_out: 0.0122 - mcc: 0.4758 - val_loss: 1.0794 - val_accuracy: 0.6350 - val_recall: 0.4350 - val_precision: 0.8700 - val_AUROC: 0.9404 - val_AUPRC: 0.7168 - val_f1_score: 0.5800 - val_balanced_accuracy: 0.7139 - val_specificity: 0.9928 - val_miss_rate: 0.5650 - val_fall_out: 0.0072 - val_mcc: 0.5888
Epoch 59/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2430 - accuracy: 0.5857 - recall: 0.3830 - precision: 0.7650 - AUROC: 0.9185 - AUPRC: 0.6441 - f1_score: 0.5104 - balanced_accuracy: 0.6850 - specificity: 0.9869 - miss_rate: 0.6170 - fall_out: 0.0131 - mcc: 0.5089 - val_loss: 1.0827 - val_accuracy: 0.6250 - val_recall: 0.4100 - val_precision: 0.8913 - val_AUROC: 0.9404 - val_AUPRC: 0.7176 - val_f1_score: 0.5616 - val_balanced_accuracy: 0.7022 - val_specificity: 0.9944 - val_miss_rate: 0.5900 - val_fall_out: 0.0056 - val_mcc: 0.5792
Epoch 60/100
7/7 [==============================] - 0s 12ms/step - loss: 1.2239 - accuracy: 0.5757 - recall: 0.3492 - precision: 0.7541 - AUROC: 0.9164 - AUPRC: 0.6302 - f1_score: 0.4773 - balanced_accuracy: 0.6683 - specificity: 0.9873 - miss_rate: 0.6508 - fall_out: 0.0127 - mcc: 0.4804 - val_loss: 1.0795 - val_accuracy: 0.6150 - val_recall: 0.4300 - val_precision: 0.8687 - val_AUROC: 0.9400 - val_AUPRC: 0.7184 - val_f1_score: 0.5753 - val_balanced_accuracy: 0.7114 - val_specificity: 0.9928 - val_miss_rate: 0.5700 - val_fall_out: 0.0072 - val_mcc: 0.5847
25/25 [==============================] - 0s 4ms/step - loss: 0.8579 - accuracy: 0.7372 - recall: 0.4981 - precision: 0.9256 - AUROC: 0.9694 - AUPRC: 0.8314 - f1_score: 0.6477 - balanced_accuracy: 0.7468 - specificity: 0.9955 - miss_rate: 0.5019 - fall_out: 0.0045 - mcc: 0.6563
7/7 [==============================] - 0s 5ms/step - loss: 1.0795 - accuracy: 0.6150 - recall: 0.4300 - precision: 0.8687 - AUROC: 0.9400 - AUPRC: 0.7184 - f1_score: 0.5753 - balanced_accuracy: 0.7114 - specificity: 0.9928 - miss_rate: 0.5700 - fall_out: 0.0072 - mcc: 0.5847
9it [01:16, 8.32s/it]
-- HOLDOUT 10 -- WINDOW window_30s
-- 20 Uncorrelated features: [Pearson+Spearman]
['mfcc16_mean', 'harmony_mean', 'mfcc10_var', 'mfcc4_mean', 'mfcc15_mean', 'mfcc19_mean', 'mfcc8_var', 'mfcc9_var', 'mfcc3_mean', 'chroma_stft_var', 'mfcc3_var', 'mfcc1_var', 'mfcc2_var', 'mfcc7_var', 'tempo', 'mfcc13_mean', 'mfcc17_mean', 'mfcc14_mean', 'perceptr_mean', 'mfcc5_var']
-- Actually uncorrelated features: [confirmed via MIC]
[]
-- 1 Correlated features: [Pearson]
['spectral_centroid_mean']
Model: "MLP"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_216 (Dense) (None, 128) 7296
dropout_167 (Dropout) (None, 128) 0
dense_217 (Dense) (None, 64) 8256
dropout_168 (Dropout) (None, 64) 0
dense_218 (Dense) (None, 64) 4160
dropout_169 (Dropout) (None, 64) 0
dense_219 (Dense) (None, 10) 650
=================================================================
Total params: 20,362
Trainable params: 20,362
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
7/7 [==============================] - 2s 85ms/step - loss: 2.7524 - accuracy: 0.0889 - recall: 0.0125 - precision: 0.1351 - AUROC: 0.5127 - AUPRC: 0.1044 - f1_score: 0.0229 - balanced_accuracy: 0.5018 - specificity: 0.9911 - miss_rate: 0.9875 - fall_out: 0.0089 - mcc: 0.0113 - val_loss: 2.4290 - val_accuracy: 0.1700 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5776 - val_AUPRC: 0.1397 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.4994 - val_specificity: 0.9989 - val_miss_rate: 1.0000 - val_fall_out: 0.0011 - val_mcc: -0.0105
Epoch 2/100
7/7 [==============================] - 0s 16ms/step - loss: 2.6320 - accuracy: 0.1126 - recall: 0.0025 - precision: 0.0377 - AUROC: 0.5185 - AUPRC: 0.1040 - f1_score: 0.0047 - balanced_accuracy: 0.4977 - specificity: 0.9929 - miss_rate: 0.9975 - fall_out: 0.0071 - mcc: -0.0170 - val_loss: 2.3406 - val_accuracy: 0.2200 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6190 - val_AUPRC: 0.1656 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.4997 - val_specificity: 0.9994 - val_miss_rate: 1.0000 - val_fall_out: 5.5556e-04 - val_mcc: -0.0075
Epoch 3/100
7/7 [==============================] - 0s 17ms/step - loss: 2.5507 - accuracy: 0.1076 - recall: 0.0013 - precision: 0.0323 - AUROC: 0.5526 - AUPRC: 0.1160 - f1_score: 0.0024 - balanced_accuracy: 0.4985 - specificity: 0.9958 - miss_rate: 0.9987 - fall_out: 0.0042 - mcc: -0.0141 - val_loss: 2.2819 - val_accuracy: 0.2600 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6623 - val_AUPRC: 0.2060 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.4994 - val_specificity: 0.9989 - val_miss_rate: 1.0000 - val_fall_out: 0.0011 - val_mcc: -0.0105
Epoch 4/100
7/7 [==============================] - 0s 15ms/step - loss: 2.4271 - accuracy: 0.1539 - recall: 0.0088 - precision: 0.1944 - AUROC: 0.5853 - AUPRC: 0.1332 - f1_score: 0.0168 - balanced_accuracy: 0.5024 - specificity: 0.9960 - miss_rate: 0.9912 - fall_out: 0.0040 - mcc: 0.0212 - val_loss: 2.2592 - val_accuracy: 0.2750 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6842 - val_AUPRC: 0.2298 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.4994 - val_specificity: 0.9989 - val_miss_rate: 1.0000 - val_fall_out: 0.0011 - val_mcc: -0.0105
Epoch 5/100
7/7 [==============================] - 0s 14ms/step - loss: 2.3219 - accuracy: 0.1927 - recall: 0.0113 - precision: 0.2571 - AUROC: 0.6152 - AUPRC: 0.1594 - f1_score: 0.0216 - balanced_accuracy: 0.5038 - specificity: 0.9964 - miss_rate: 0.9887 - fall_out: 0.0036 - mcc: 0.0347 - val_loss: 2.2005 - val_accuracy: 0.3200 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7211 - val_AUPRC: 0.2611 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.4997 - val_specificity: 0.9994 - val_miss_rate: 1.0000 - val_fall_out: 5.5556e-04 - val_mcc: -0.0075
Epoch 6/100
7/7 [==============================] - 0s 14ms/step - loss: 2.2587 - accuracy: 0.1990 - recall: 0.0150 - precision: 0.3333 - AUROC: 0.6403 - AUPRC: 0.1779 - f1_score: 0.0287 - balanced_accuracy: 0.5058 - specificity: 0.9967 - miss_rate: 0.9850 - fall_out: 0.0033 - mcc: 0.0523 - val_loss: 2.1587 - val_accuracy: 0.3150 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7499 - val_AUPRC: 0.2837 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.4994 - val_specificity: 0.9989 - val_miss_rate: 1.0000 - val_fall_out: 0.0011 - val_mcc: -0.0105
Epoch 7/100
7/7 [==============================] - 0s 13ms/step - loss: 2.2330 - accuracy: 0.2441 - recall: 0.0113 - precision: 0.3000 - AUROC: 0.6585 - AUPRC: 0.2044 - f1_score: 0.0217 - balanced_accuracy: 0.5042 - specificity: 0.9971 - miss_rate: 0.9887 - fall_out: 0.0029 - mcc: 0.0409 - val_loss: 2.1133 - val_accuracy: 0.3700 - val_recall: 0.0100 - val_precision: 0.5000 - val_AUROC: 0.7717 - val_AUPRC: 0.3084 - val_f1_score: 0.0196 - val_balanced_accuracy: 0.5044 - val_specificity: 0.9989 - val_miss_rate: 0.9900 - val_fall_out: 0.0011 - val_mcc: 0.0597
Epoch 8/100
7/7 [==============================] - 0s 14ms/step - loss: 2.2009 - accuracy: 0.2178 - recall: 0.0263 - precision: 0.5526 - AUROC: 0.6688 - AUPRC: 0.2087 - f1_score: 0.0502 - balanced_accuracy: 0.5120 - specificity: 0.9976 - miss_rate: 0.9737 - fall_out: 0.0024 - mcc: 0.1043 - val_loss: 2.0726 - val_accuracy: 0.3700 - val_recall: 0.0150 - val_precision: 0.7500 - val_AUROC: 0.7873 - val_AUPRC: 0.3294 - val_f1_score: 0.0294 - val_balanced_accuracy: 0.5072 - val_specificity: 0.9994 - val_miss_rate: 0.9850 - val_fall_out: 5.5556e-04 - val_mcc: 0.0970
Epoch 9/100
7/7 [==============================] - 0s 15ms/step - loss: 2.1318 - accuracy: 0.2628 - recall: 0.0401 - precision: 0.5333 - AUROC: 0.6983 - AUPRC: 0.2408 - f1_score: 0.0745 - balanced_accuracy: 0.5181 - specificity: 0.9961 - miss_rate: 0.9599 - fall_out: 0.0039 - mcc: 0.1256 - val_loss: 2.0397 - val_accuracy: 0.3900 - val_recall: 0.0250 - val_precision: 0.7143 - val_AUROC: 0.7975 - val_AUPRC: 0.3413 - val_f1_score: 0.0483 - val_balanced_accuracy: 0.5119 - val_specificity: 0.9989 - val_miss_rate: 0.9750 - val_fall_out: 0.0011 - val_mcc: 0.1214
Epoch 10/100
7/7 [==============================] - 0s 12ms/step - loss: 2.1597 - accuracy: 0.2703 - recall: 0.0388 - precision: 0.5536 - AUROC: 0.6985 - AUPRC: 0.2433 - f1_score: 0.0725 - balanced_accuracy: 0.5177 - specificity: 0.9965 - miss_rate: 0.9612 - fall_out: 0.0035 - mcc: 0.1270 - val_loss: 1.9892 - val_accuracy: 0.4150 - val_recall: 0.0250 - val_precision: 0.6250 - val_AUROC: 0.8045 - val_AUPRC: 0.3628 - val_f1_score: 0.0481 - val_balanced_accuracy: 0.5117 - val_specificity: 0.9983 - val_miss_rate: 0.9750 - val_fall_out: 0.0017 - val_mcc: 0.1109
Epoch 11/100
7/7 [==============================] - 0s 12ms/step - loss: 2.0809 - accuracy: 0.2566 - recall: 0.0401 - precision: 0.5079 - AUROC: 0.7162 - AUPRC: 0.2494 - f1_score: 0.0742 - balanced_accuracy: 0.5179 - specificity: 0.9957 - miss_rate: 0.9599 - fall_out: 0.0043 - mcc: 0.1212 - val_loss: 1.9375 - val_accuracy: 0.4050 - val_recall: 0.0400 - val_precision: 0.7273 - val_AUROC: 0.8109 - val_AUPRC: 0.3820 - val_f1_score: 0.0758 - val_balanced_accuracy: 0.5192 - val_specificity: 0.9983 - val_miss_rate: 0.9600 - val_fall_out: 0.0017 - val_mcc: 0.1555
Epoch 12/100
7/7 [==============================] - 0s 13ms/step - loss: 1.9987 - accuracy: 0.3242 - recall: 0.0713 - precision: 0.6264 - AUROC: 0.7549 - AUPRC: 0.3037 - f1_score: 0.1281 - balanced_accuracy: 0.5333 - specificity: 0.9953 - miss_rate: 0.9287 - fall_out: 0.0047 - mcc: 0.1883 - val_loss: 1.8965 - val_accuracy: 0.4200 - val_recall: 0.0600 - val_precision: 0.7500 - val_AUROC: 0.8150 - val_AUPRC: 0.3983 - val_f1_score: 0.1111 - val_balanced_accuracy: 0.5289 - val_specificity: 0.9978 - val_miss_rate: 0.9400 - val_fall_out: 0.0022 - val_mcc: 0.1946
Epoch 13/100
7/7 [==============================] - 0s 12ms/step - loss: 2.0231 - accuracy: 0.3091 - recall: 0.0738 - precision: 0.5413 - AUROC: 0.7414 - AUPRC: 0.2852 - f1_score: 0.1300 - balanced_accuracy: 0.5334 - specificity: 0.9930 - miss_rate: 0.9262 - fall_out: 0.0070 - mcc: 0.1730 - val_loss: 1.8717 - val_accuracy: 0.4350 - val_recall: 0.0700 - val_precision: 0.7368 - val_AUROC: 0.8245 - val_AUPRC: 0.4156 - val_f1_score: 0.1279 - val_balanced_accuracy: 0.5336 - val_specificity: 0.9972 - val_miss_rate: 0.9300 - val_fall_out: 0.0028 - val_mcc: 0.2079
Epoch 14/100
7/7 [==============================] - 0s 12ms/step - loss: 1.9517 - accuracy: 0.3179 - recall: 0.0676 - precision: 0.5455 - AUROC: 0.7630 - AUPRC: 0.3019 - f1_score: 0.1203 - balanced_accuracy: 0.5307 - specificity: 0.9937 - miss_rate: 0.9324 - fall_out: 0.0063 - mcc: 0.1663 - val_loss: 1.8174 - val_accuracy: 0.4600 - val_recall: 0.0750 - val_precision: 0.8333 - val_AUROC: 0.8347 - val_AUPRC: 0.4478 - val_f1_score: 0.1376 - val_balanced_accuracy: 0.5367 - val_specificity: 0.9983 - val_miss_rate: 0.9250 - val_fall_out: 0.0017 - val_mcc: 0.2330
Epoch 15/100
7/7 [==============================] - 0s 12ms/step - loss: 1.9010 - accuracy: 0.3417 - recall: 0.0926 - precision: 0.6167 - AUROC: 0.7824 - AUPRC: 0.3398 - f1_score: 0.1610 - balanced_accuracy: 0.5431 - specificity: 0.9936 - miss_rate: 0.9074 - fall_out: 0.0064 - mcc: 0.2127 - val_loss: 1.7822 - val_accuracy: 0.4650 - val_recall: 0.1250 - val_precision: 0.8929 - val_AUROC: 0.8448 - val_AUPRC: 0.4608 - val_f1_score: 0.2193 - val_balanced_accuracy: 0.5617 - val_specificity: 0.9983 - val_miss_rate: 0.8750 - val_fall_out: 0.0017 - val_mcc: 0.3149
Epoch 16/100
7/7 [==============================] - 0s 12ms/step - loss: 1.8874 - accuracy: 0.3304 - recall: 0.1039 - precision: 0.6535 - AUROC: 0.7805 - AUPRC: 0.3338 - f1_score: 0.1793 - balanced_accuracy: 0.5489 - specificity: 0.9939 - miss_rate: 0.8961 - fall_out: 0.0061 - mcc: 0.2345 - val_loss: 1.7367 - val_accuracy: 0.4800 - val_recall: 0.1500 - val_precision: 0.9375 - val_AUROC: 0.8543 - val_AUPRC: 0.4761 - val_f1_score: 0.2586 - val_balanced_accuracy: 0.5744 - val_specificity: 0.9989 - val_miss_rate: 0.8500 - val_fall_out: 0.0011 - val_mcc: 0.3560
Epoch 17/100
7/7 [==============================] - 0s 13ms/step - loss: 1.8198 - accuracy: 0.3792 - recall: 0.1389 - precision: 0.7208 - AUROC: 0.7972 - AUPRC: 0.3869 - f1_score: 0.2329 - balanced_accuracy: 0.5665 - specificity: 0.9940 - miss_rate: 0.8611 - fall_out: 0.0060 - mcc: 0.2901 - val_loss: 1.7239 - val_accuracy: 0.4950 - val_recall: 0.1600 - val_precision: 0.9143 - val_AUROC: 0.8584 - val_AUPRC: 0.4815 - val_f1_score: 0.2723 - val_balanced_accuracy: 0.5792 - val_specificity: 0.9983 - val_miss_rate: 0.8400 - val_fall_out: 0.0017 - val_mcc: 0.3622
Epoch 18/100
7/7 [==============================] - 0s 12ms/step - loss: 1.8336 - accuracy: 0.3705 - recall: 0.1202 - precision: 0.6621 - AUROC: 0.8025 - AUPRC: 0.3769 - f1_score: 0.2034 - balanced_accuracy: 0.5567 - specificity: 0.9932 - miss_rate: 0.8798 - fall_out: 0.0068 - mcc: 0.2547 - val_loss: 1.7041 - val_accuracy: 0.5000 - val_recall: 0.1800 - val_precision: 0.9000 - val_AUROC: 0.8642 - val_AUPRC: 0.4947 - val_f1_score: 0.3000 - val_balanced_accuracy: 0.5889 - val_specificity: 0.9978 - val_miss_rate: 0.8200 - val_fall_out: 0.0022 - val_mcc: 0.3810
Epoch 19/100
7/7 [==============================] - 0s 13ms/step - loss: 1.8010 - accuracy: 0.3742 - recall: 0.1539 - precision: 0.7029 - AUROC: 0.8096 - AUPRC: 0.3873 - f1_score: 0.2526 - balanced_accuracy: 0.5734 - specificity: 0.9928 - miss_rate: 0.8461 - fall_out: 0.0072 - mcc: 0.3007 - val_loss: 1.6561 - val_accuracy: 0.5050 - val_recall: 0.1950 - val_precision: 0.9286 - val_AUROC: 0.8734 - val_AUPRC: 0.5136 - val_f1_score: 0.3223 - val_balanced_accuracy: 0.5967 - val_specificity: 0.9983 - val_miss_rate: 0.8050 - val_fall_out: 0.0017 - val_mcc: 0.4045
Epoch 20/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7941 - accuracy: 0.3692 - recall: 0.1364 - precision: 0.6193 - AUROC: 0.8164 - AUPRC: 0.3745 - f1_score: 0.2236 - balanced_accuracy: 0.5636 - specificity: 0.9907 - miss_rate: 0.8636 - fall_out: 0.0093 - mcc: 0.2598 - val_loss: 1.6215 - val_accuracy: 0.5250 - val_recall: 0.2000 - val_precision: 0.9091 - val_AUROC: 0.8761 - val_AUPRC: 0.5209 - val_f1_score: 0.3279 - val_balanced_accuracy: 0.5989 - val_specificity: 0.9978 - val_miss_rate: 0.8000 - val_fall_out: 0.0022 - val_mcc: 0.4045
Epoch 21/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7645 - accuracy: 0.3767 - recall: 0.1477 - precision: 0.6743 - AUROC: 0.8143 - AUPRC: 0.4014 - f1_score: 0.2423 - balanced_accuracy: 0.5699 - specificity: 0.9921 - miss_rate: 0.8523 - fall_out: 0.0079 - mcc: 0.2865 - val_loss: 1.5934 - val_accuracy: 0.5050 - val_recall: 0.2050 - val_precision: 0.9111 - val_AUROC: 0.8787 - val_AUPRC: 0.5280 - val_f1_score: 0.3347 - val_balanced_accuracy: 0.6014 - val_specificity: 0.9978 - val_miss_rate: 0.7950 - val_fall_out: 0.0022 - val_mcc: 0.4102
Epoch 22/100
7/7 [==============================] - 0s 12ms/step - loss: 1.7220 - accuracy: 0.3930 - recall: 0.1577 - precision: 0.6597 - AUROC: 0.8261 - AUPRC: 0.4140 - f1_score: 0.2545 - balanced_accuracy: 0.5743 - specificity: 0.9910 - miss_rate: 0.8423 - fall_out: 0.0090 - mcc: 0.2920 - val_loss: 1.5549 - val_accuracy: 0.5350 - val_recall: 0.2200 - val_precision: 0.8800 - val_AUROC: 0.8811 - val_AUPRC: 0.5494 - val_f1_score: 0.3520 - val_balanced_accuracy: 0.6083 - val_specificity: 0.9967 - val_miss_rate: 0.7800 - val_fall_out: 0.0033 - val_mcc: 0.4163
Epoch 23/100
7/7 [==============================] - 0s 12ms/step - loss: 1.6853 - accuracy: 0.4355 - recall: 0.1852 - precision: 0.7014 - AUROC: 0.8359 - AUPRC: 0.4486 - f1_score: 0.2931 - balanced_accuracy: 0.5882 - specificity: 0.9912 - miss_rate: 0.8148 - fall_out: 0.0088 - mcc: 0.3302 - val_loss: 1.5198 - val_accuracy: 0.5400 - val_recall: 0.2300 - val_precision: 0.8519 - val_AUROC: 0.8854 - val_AUPRC: 0.5581 - val_f1_score: 0.3622 - val_balanced_accuracy: 0.6128 - val_specificity: 0.9956 - val_miss_rate: 0.7700 - val_fall_out: 0.0044 - val_mcc: 0.4175
Epoch 24/100
7/7 [==============================] - 0s 12ms/step - loss: 1.6694 - accuracy: 0.4255 - recall: 0.1727 - precision: 0.6479 - AUROC: 0.8339 - AUPRC: 0.4392 - f1_score: 0.2727 - balanced_accuracy: 0.5811 - specificity: 0.9896 - miss_rate: 0.8273 - fall_out: 0.0104 - mcc: 0.3022 - val_loss: 1.4979 - val_accuracy: 0.5450 - val_recall: 0.2300 - val_precision: 0.8364 - val_AUROC: 0.8885 - val_AUPRC: 0.5662 - val_f1_score: 0.3608 - val_balanced_accuracy: 0.6125 - val_specificity: 0.9950 - val_miss_rate: 0.7700 - val_fall_out: 0.0050 - val_mcc: 0.4128
Epoch 25/100
7/7 [==============================] - 0s 12ms/step - loss: 1.6434 - accuracy: 0.4531 - recall: 0.2065 - precision: 0.6846 - AUROC: 0.8410 - AUPRC: 0.4580 - f1_score: 0.3173 - balanced_accuracy: 0.5980 - specificity: 0.9894 - miss_rate: 0.7935 - fall_out: 0.0106 - mcc: 0.3437 - val_loss: 1.4649 - val_accuracy: 0.5350 - val_recall: 0.2450 - val_precision: 0.8305 - val_AUROC: 0.8928 - val_AUPRC: 0.5788 - val_f1_score: 0.3784 - val_balanced_accuracy: 0.6197 - val_specificity: 0.9944 - val_miss_rate: 0.7550 - val_fall_out: 0.0056 - val_mcc: 0.4245
Epoch 26/100
7/7 [==============================] - 0s 12ms/step - loss: 1.6244 - accuracy: 0.4293 - recall: 0.2040 - precision: 0.6849 - AUROC: 0.8453 - AUPRC: 0.4527 - f1_score: 0.3144 - balanced_accuracy: 0.5968 - specificity: 0.9896 - miss_rate: 0.7960 - fall_out: 0.0104 - mcc: 0.3416 - val_loss: 1.4376 - val_accuracy: 0.5550 - val_recall: 0.2450 - val_precision: 0.8448 - val_AUROC: 0.8975 - val_AUPRC: 0.5924 - val_f1_score: 0.3798 - val_balanced_accuracy: 0.6200 - val_specificity: 0.9950 - val_miss_rate: 0.7550 - val_fall_out: 0.0050 - val_mcc: 0.4291
Epoch 27/100
7/7 [==============================] - 0s 12ms/step - loss: 1.6140 - accuracy: 0.4631 - recall: 0.2053 - precision: 0.6805 - AUROC: 0.8561 - AUPRC: 0.4734 - f1_score: 0.3154 - balanced_accuracy: 0.5973 - specificity: 0.9893 - miss_rate: 0.7947 - fall_out: 0.0107 - mcc: 0.3412 - val_loss: 1.4362 - val_accuracy: 0.5550 - val_recall: 0.2450 - val_precision: 0.8305 - val_AUROC: 0.9005 - val_AUPRC: 0.5932 - val_f1_score: 0.3784 - val_balanced_accuracy: 0.6197 - val_specificity: 0.9944 - val_miss_rate: 0.7550 - val_fall_out: 0.0056 - val_mcc: 0.4245
Epoch 28/100
7/7 [==============================] - 0s 12ms/step - loss: 1.5645 - accuracy: 0.4593 - recall: 0.2203 - precision: 0.7068 - AUROC: 0.8668 - AUPRC: 0.4926 - f1_score: 0.3359 - balanced_accuracy: 0.6051 - specificity: 0.9898 - miss_rate: 0.7797 - fall_out: 0.0102 - mcc: 0.3628 - val_loss: 1.4174 - val_accuracy: 0.5600 - val_recall: 0.2450 - val_precision: 0.8305 - val_AUROC: 0.9045 - val_AUPRC: 0.5933 - val_f1_score: 0.3784 - val_balanced_accuracy: 0.6197 - val_specificity: 0.9944 - val_miss_rate: 0.7550 - val_fall_out: 0.0056 - val_mcc: 0.4245
Epoch 29/100
7/7 [==============================] - 0s 13ms/step - loss: 1.5528 - accuracy: 0.4581 - recall: 0.2240 - precision: 0.6679 - AUROC: 0.8641 - AUPRC: 0.4853 - f1_score: 0.3355 - balanced_accuracy: 0.6058 - specificity: 0.9876 - miss_rate: 0.7760 - fall_out: 0.0124 - mcc: 0.3527 - val_loss: 1.4073 - val_accuracy: 0.5450 - val_recall: 0.2450 - val_precision: 0.8305 - val_AUROC: 0.9057 - val_AUPRC: 0.5964 - val_f1_score: 0.3784 - val_balanced_accuracy: 0.6197 - val_specificity: 0.9944 - val_miss_rate: 0.7550 - val_fall_out: 0.0056 - val_mcc: 0.4245
Epoch 30/100
7/7 [==============================] - 0s 14ms/step - loss: 1.5930 - accuracy: 0.4418 - recall: 0.1977 - precision: 0.6529 - AUROC: 0.8599 - AUPRC: 0.4736 - f1_score: 0.3036 - balanced_accuracy: 0.5930 - specificity: 0.9883 - miss_rate: 0.8023 - fall_out: 0.0117 - mcc: 0.3257 - val_loss: 1.4018 - val_accuracy: 0.5650 - val_recall: 0.2400 - val_precision: 0.8421 - val_AUROC: 0.9057 - val_AUPRC: 0.6001 - val_f1_score: 0.3735 - val_balanced_accuracy: 0.6175 - val_specificity: 0.9950 - val_miss_rate: 0.7600 - val_fall_out: 0.0050 - val_mcc: 0.4237
Epoch 31/100
7/7 [==============================] - 0s 14ms/step - loss: 1.4660 - accuracy: 0.5006 - recall: 0.2516 - precision: 0.7390 - AUROC: 0.8810 - AUPRC: 0.5376 - f1_score: 0.3754 - balanced_accuracy: 0.6208 - specificity: 0.9901 - miss_rate: 0.7484 - fall_out: 0.0099 - mcc: 0.3998 - val_loss: 1.3796 - val_accuracy: 0.5600 - val_recall: 0.2600 - val_precision: 0.8525 - val_AUROC: 0.9072 - val_AUPRC: 0.6105 - val_f1_score: 0.3985 - val_balanced_accuracy: 0.6275 - val_specificity: 0.9950 - val_miss_rate: 0.7400 - val_fall_out: 0.0050 - val_mcc: 0.4449
Epoch 32/100
7/7 [==============================] - 0s 14ms/step - loss: 1.4637 - accuracy: 0.5044 - recall: 0.2766 - precision: 0.7543 - AUROC: 0.8827 - AUPRC: 0.5460 - f1_score: 0.4048 - balanced_accuracy: 0.6333 - specificity: 0.9900 - miss_rate: 0.7234 - fall_out: 0.0100 - mcc: 0.4255 - val_loss: 1.3610 - val_accuracy: 0.5500 - val_recall: 0.2650 - val_precision: 0.8413 - val_AUROC: 0.9083 - val_AUPRC: 0.6198 - val_f1_score: 0.4030 - val_balanced_accuracy: 0.6297 - val_specificity: 0.9944 - val_miss_rate: 0.7350 - val_fall_out: 0.0056 - val_mcc: 0.4456
Epoch 33/100
7/7 [==============================] - 0s 14ms/step - loss: 1.4700 - accuracy: 0.4831 - recall: 0.2528 - precision: 0.7138 - AUROC: 0.8815 - AUPRC: 0.5347 - f1_score: 0.3734 - balanced_accuracy: 0.6208 - specificity: 0.9887 - miss_rate: 0.7472 - fall_out: 0.0113 - mcc: 0.3921 - val_loss: 1.3382 - val_accuracy: 0.5750 - val_recall: 0.2900 - val_precision: 0.8169 - val_AUROC: 0.9105 - val_AUPRC: 0.6270 - val_f1_score: 0.4280 - val_balanced_accuracy: 0.6414 - val_specificity: 0.9928 - val_miss_rate: 0.7100 - val_fall_out: 0.0072 - val_mcc: 0.4585
Epoch 34/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4389 - accuracy: 0.5131 - recall: 0.2641 - precision: 0.7251 - AUROC: 0.8846 - AUPRC: 0.5487 - f1_score: 0.3872 - balanced_accuracy: 0.6265 - specificity: 0.9889 - miss_rate: 0.7359 - fall_out: 0.0111 - mcc: 0.4051 - val_loss: 1.3115 - val_accuracy: 0.5800 - val_recall: 0.3200 - val_precision: 0.8205 - val_AUROC: 0.9124 - val_AUPRC: 0.6341 - val_f1_score: 0.4604 - val_balanced_accuracy: 0.6561 - val_specificity: 0.9922 - val_miss_rate: 0.6800 - val_fall_out: 0.0078 - val_mcc: 0.4838
Epoch 35/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4296 - accuracy: 0.4806 - recall: 0.2728 - precision: 0.6987 - AUROC: 0.8846 - AUPRC: 0.5299 - f1_score: 0.3924 - balanced_accuracy: 0.6299 - specificity: 0.9869 - miss_rate: 0.7272 - fall_out: 0.0131 - mcc: 0.4023 - val_loss: 1.2920 - val_accuracy: 0.5650 - val_recall: 0.3250 - val_precision: 0.8333 - val_AUROC: 0.9146 - val_AUPRC: 0.6386 - val_f1_score: 0.4676 - val_balanced_accuracy: 0.6589 - val_specificity: 0.9928 - val_miss_rate: 0.6750 - val_fall_out: 0.0072 - val_mcc: 0.4924
Epoch 36/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4019 - accuracy: 0.4981 - recall: 0.2916 - precision: 0.7191 - AUROC: 0.8953 - AUPRC: 0.5524 - f1_score: 0.4150 - balanced_accuracy: 0.6395 - specificity: 0.9873 - miss_rate: 0.7084 - fall_out: 0.0127 - mcc: 0.4243 - val_loss: 1.2754 - val_accuracy: 0.5600 - val_recall: 0.3300 - val_precision: 0.8354 - val_AUROC: 0.9172 - val_AUPRC: 0.6426 - val_f1_score: 0.4731 - val_balanced_accuracy: 0.6614 - val_specificity: 0.9928 - val_miss_rate: 0.6700 - val_fall_out: 0.0072 - val_mcc: 0.4971
Epoch 37/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3954 - accuracy: 0.5244 - recall: 0.2866 - precision: 0.6939 - AUROC: 0.8926 - AUPRC: 0.5610 - f1_score: 0.4057 - balanced_accuracy: 0.6363 - specificity: 0.9860 - miss_rate: 0.7134 - fall_out: 0.0140 - mcc: 0.4109 - val_loss: 1.2592 - val_accuracy: 0.5800 - val_recall: 0.3350 - val_precision: 0.8375 - val_AUROC: 0.9195 - val_AUPRC: 0.6466 - val_f1_score: 0.4786 - val_balanced_accuracy: 0.6639 - val_specificity: 0.9928 - val_miss_rate: 0.6650 - val_fall_out: 0.0072 - val_mcc: 0.5018
Epoch 38/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4029 - accuracy: 0.5144 - recall: 0.2866 - precision: 0.7316 - AUROC: 0.8913 - AUPRC: 0.5632 - f1_score: 0.4119 - balanced_accuracy: 0.6375 - specificity: 0.9883 - miss_rate: 0.7134 - fall_out: 0.0117 - mcc: 0.4251 - val_loss: 1.2446 - val_accuracy: 0.5900 - val_recall: 0.3350 - val_precision: 0.8481 - val_AUROC: 0.9220 - val_AUPRC: 0.6554 - val_f1_score: 0.4803 - val_balanced_accuracy: 0.6642 - val_specificity: 0.9933 - val_miss_rate: 0.6650 - val_fall_out: 0.0067 - val_mcc: 0.5057
Epoch 39/100
7/7 [==============================] - 0s 12ms/step - loss: 1.4130 - accuracy: 0.5156 - recall: 0.2954 - precision: 0.7284 - AUROC: 0.8940 - AUPRC: 0.5687 - f1_score: 0.4203 - balanced_accuracy: 0.6416 - specificity: 0.9878 - miss_rate: 0.7046 - fall_out: 0.0122 - mcc: 0.4306 - val_loss: 1.2392 - val_accuracy: 0.6100 - val_recall: 0.3350 - val_precision: 0.8272 - val_AUROC: 0.9218 - val_AUPRC: 0.6610 - val_f1_score: 0.4769 - val_balanced_accuracy: 0.6636 - val_specificity: 0.9922 - val_miss_rate: 0.6650 - val_fall_out: 0.0078 - val_mcc: 0.4980
Epoch 40/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3464 - accuracy: 0.5357 - recall: 0.2916 - precision: 0.7169 - AUROC: 0.8986 - AUPRC: 0.5777 - f1_score: 0.4146 - balanced_accuracy: 0.6394 - specificity: 0.9872 - miss_rate: 0.7084 - fall_out: 0.0128 - mcc: 0.4234 - val_loss: 1.2317 - val_accuracy: 0.6000 - val_recall: 0.3350 - val_precision: 0.8171 - val_AUROC: 0.9224 - val_AUPRC: 0.6604 - val_f1_score: 0.4752 - val_balanced_accuracy: 0.6633 - val_specificity: 0.9917 - val_miss_rate: 0.6650 - val_fall_out: 0.0083 - val_mcc: 0.4942
Epoch 41/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3559 - accuracy: 0.5357 - recall: 0.3091 - precision: 0.7308 - AUROC: 0.8954 - AUPRC: 0.5797 - f1_score: 0.4345 - balanced_accuracy: 0.6482 - specificity: 0.9873 - miss_rate: 0.6909 - fall_out: 0.0127 - mcc: 0.4419 - val_loss: 1.2459 - val_accuracy: 0.5950 - val_recall: 0.3500 - val_precision: 0.8235 - val_AUROC: 0.9228 - val_AUPRC: 0.6610 - val_f1_score: 0.4912 - val_balanced_accuracy: 0.6708 - val_specificity: 0.9917 - val_miss_rate: 0.6500 - val_fall_out: 0.0083 - val_mcc: 0.5081
Epoch 42/100
7/7 [==============================] - 0s 12ms/step - loss: 1.3555 - accuracy: 0.5232 - recall: 0.3091 - precision: 0.7180 - AUROC: 0.8996 - AUPRC: 0.5784 - f1_score: 0.4322 - balanced_accuracy: 0.6478 - specificity: 0.9865 - miss_rate: 0.6909 - fall_out: 0.0135 - mcc: 0.4370 - val_loss: 1.2521 - val_accuracy: 0.5950 - val_recall: 0.3400 - val_precision: 0.8095 - val_AUROC: 0.9233 - val_AUPRC: 0.6447 - val_f1_score: 0.4789 - val_balanced_accuracy: 0.6656 - val_specificity: 0.9911 - val_miss_rate: 0.6600 - val_fall_out: 0.0089 - val_mcc: 0.4952
25/25 [==============================] - 0s 4ms/step - loss: 1.0005 - accuracy: 0.7184 - recall: 0.4068 - precision: 0.8953 - AUROC: 0.9576 - AUPRC: 0.7809 - f1_score: 0.5594 - balanced_accuracy: 0.7007 - specificity: 0.9947 - miss_rate: 0.5932 - fall_out: 0.0053 - mcc: 0.5784
7/7 [==============================] - 0s 5ms/step - loss: 1.2521 - accuracy: 0.5950 - recall: 0.3400 - precision: 0.8095 - AUROC: 0.9233 - AUPRC: 0.6447 - f1_score: 0.4789 - balanced_accuracy: 0.6656 - specificity: 0.9911 - miss_rate: 0.6600 - fall_out: 0.0089 - mcc: 0.4952
10it [01:22, 8.21s/it] 0it [00:00, ?it/s]
-- HOLDOUT 1 -- WINDOW window_3s
-- 6 Uncorrelated features: [Pearson+Spearman]
['mfcc16_mean', 'tempo', 'mfcc10_var', 'mfcc19_mean', 'mfcc11_var', 'mfcc17_mean']
-- Actually uncorrelated features: [confirmed via MIC]
[]
-- 1 Correlated features: [Pearson]
['spectral_centroid_mean']
Model: "MLP"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_220 (Dense) (None, 256) 14592
dropout_170 (Dropout) (None, 256) 0
dense_221 (Dense) (None, 256) 65792
dropout_171 (Dropout) (None, 256) 0
dense_222 (Dense) (None, 128) 32896
dropout_172 (Dropout) (None, 128) 0
dense_223 (Dense) (None, 128) 16512
dropout_173 (Dropout) (None, 128) 0
dense_224 (Dense) (None, 10) 1290
=================================================================
Total params: 131,082
Trainable params: 131,082
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 2s 18ms/step - loss: 2.2232 - accuracy: 0.2133 - recall: 0.0263 - precision: 0.4506 - AUROC: 0.6510 - AUPRC: 0.1910 - f1_score: 0.0497 - balanced_accuracy: 0.5114 - specificity: 0.9964 - miss_rate: 0.9737 - fall_out: 0.0036 - mcc: 0.0896 - val_loss: 1.6996 - val_accuracy: 0.3838 - val_recall: 0.1363 - val_precision: 0.8168 - val_AUROC: 0.8502 - val_AUPRC: 0.4441 - val_f1_score: 0.2336 - val_balanced_accuracy: 0.5664 - val_specificity: 0.9966 - val_miss_rate: 0.8637 - val_fall_out: 0.0034 - val_mcc: 0.3112
Epoch 2/100
63/63 [==============================] - 1s 10ms/step - loss: 1.7717 - accuracy: 0.3567 - recall: 0.1448 - precision: 0.5999 - AUROC: 0.8150 - AUPRC: 0.3719 - f1_score: 0.2333 - balanced_accuracy: 0.5670 - specificity: 0.9893 - miss_rate: 0.8552 - fall_out: 0.0107 - mcc: 0.2621 - val_loss: 1.4377 - val_accuracy: 0.4734 - val_recall: 0.2630 - val_precision: 0.7405 - val_AUROC: 0.8920 - val_AUPRC: 0.5467 - val_f1_score: 0.3882 - val_balanced_accuracy: 0.6264 - val_specificity: 0.9898 - val_miss_rate: 0.7370 - val_fall_out: 0.0102 - val_mcc: 0.4097
Epoch 3/100
63/63 [==============================] - 1s 9ms/step - loss: 1.5347 - accuracy: 0.4584 - recall: 0.2331 - precision: 0.6799 - AUROC: 0.8679 - AUPRC: 0.4849 - f1_score: 0.3472 - balanced_accuracy: 0.6105 - specificity: 0.9878 - miss_rate: 0.7669 - fall_out: 0.0122 - mcc: 0.3642 - val_loss: 1.2188 - val_accuracy: 0.5731 - val_recall: 0.3201 - val_precision: 0.7928 - val_AUROC: 0.9236 - val_AUPRC: 0.6403 - val_f1_score: 0.4561 - val_balanced_accuracy: 0.6554 - val_specificity: 0.9907 - val_miss_rate: 0.6799 - val_fall_out: 0.0093 - val_mcc: 0.4737
Epoch 4/100
63/63 [==============================] - 1s 10ms/step - loss: 1.4023 - accuracy: 0.5096 - recall: 0.2961 - precision: 0.6931 - AUROC: 0.8900 - AUPRC: 0.5410 - f1_score: 0.4149 - balanced_accuracy: 0.6408 - specificity: 0.9854 - miss_rate: 0.7039 - fall_out: 0.0146 - mcc: 0.4176 - val_loss: 1.1392 - val_accuracy: 0.6162 - val_recall: 0.3592 - val_precision: 0.7853 - val_AUROC: 0.9319 - val_AUPRC: 0.6739 - val_f1_score: 0.4930 - val_balanced_accuracy: 0.6742 - val_specificity: 0.9891 - val_miss_rate: 0.6408 - val_fall_out: 0.0109 - val_mcc: 0.5001
Epoch 5/100
63/63 [==============================] - 1s 10ms/step - loss: 1.2783 - accuracy: 0.5620 - recall: 0.3538 - precision: 0.7364 - AUROC: 0.9094 - AUPRC: 0.6052 - f1_score: 0.4780 - balanced_accuracy: 0.6699 - specificity: 0.9859 - miss_rate: 0.6462 - fall_out: 0.0141 - mcc: 0.4766 - val_loss: 0.9994 - val_accuracy: 0.6688 - val_recall: 0.4469 - val_precision: 0.8455 - val_AUROC: 0.9488 - val_AUPRC: 0.7482 - val_f1_score: 0.5847 - val_balanced_accuracy: 0.7189 - val_specificity: 0.9909 - val_miss_rate: 0.5531 - val_fall_out: 0.0091 - val_mcc: 0.5870
Epoch 6/100
63/63 [==============================] - 1s 10ms/step - loss: 1.1899 - accuracy: 0.5907 - recall: 0.4012 - precision: 0.7552 - AUROC: 0.9213 - AUPRC: 0.6472 - f1_score: 0.5240 - balanced_accuracy: 0.6934 - specificity: 0.9856 - miss_rate: 0.5988 - fall_out: 0.0144 - mcc: 0.5173 - val_loss: 0.9212 - val_accuracy: 0.6834 - val_recall: 0.5110 - val_precision: 0.8409 - val_AUROC: 0.9550 - val_AUPRC: 0.7760 - val_f1_score: 0.6357 - val_balanced_accuracy: 0.7501 - val_specificity: 0.9893 - val_miss_rate: 0.4890 - val_fall_out: 0.0107 - val_mcc: 0.6282
Epoch 7/100
63/63 [==============================] - 1s 9ms/step - loss: 1.1102 - accuracy: 0.6304 - recall: 0.4595 - precision: 0.7663 - AUROC: 0.9306 - AUPRC: 0.6881 - f1_score: 0.5745 - balanced_accuracy: 0.7220 - specificity: 0.9844 - miss_rate: 0.5405 - fall_out: 0.0156 - mcc: 0.5610 - val_loss: 0.8470 - val_accuracy: 0.7169 - val_recall: 0.5526 - val_precision: 0.8712 - val_AUROC: 0.9624 - val_AUPRC: 0.8075 - val_f1_score: 0.6763 - val_balanced_accuracy: 0.7718 - val_specificity: 0.9909 - val_miss_rate: 0.4474 - val_fall_out: 0.0091 - val_mcc: 0.6690
Epoch 8/100
63/63 [==============================] - 1s 10ms/step - loss: 1.0644 - accuracy: 0.6448 - recall: 0.4863 - precision: 0.7774 - AUROC: 0.9357 - AUPRC: 0.7086 - f1_score: 0.5984 - balanced_accuracy: 0.7354 - specificity: 0.9845 - miss_rate: 0.5137 - fall_out: 0.0155 - mcc: 0.5833 - val_loss: 0.7994 - val_accuracy: 0.7305 - val_recall: 0.5686 - val_precision: 0.8717 - val_AUROC: 0.9672 - val_AUPRC: 0.8277 - val_f1_score: 0.6883 - val_balanced_accuracy: 0.7797 - val_specificity: 0.9907 - val_miss_rate: 0.4314 - val_fall_out: 0.0093 - val_mcc: 0.6795
Epoch 9/100
63/63 [==============================] - 1s 10ms/step - loss: 0.9984 - accuracy: 0.6662 - recall: 0.5182 - precision: 0.7901 - AUROC: 0.9430 - AUPRC: 0.7330 - f1_score: 0.6259 - balanced_accuracy: 0.7514 - specificity: 0.9847 - miss_rate: 0.4818 - fall_out: 0.0153 - mcc: 0.6094 - val_loss: 0.7612 - val_accuracy: 0.7370 - val_recall: 0.6107 - val_precision: 0.8639 - val_AUROC: 0.9696 - val_AUPRC: 0.8365 - val_f1_score: 0.7156 - val_balanced_accuracy: 0.8000 - val_specificity: 0.9893 - val_miss_rate: 0.3893 - val_fall_out: 0.0107 - val_mcc: 0.7023
Epoch 10/100
63/63 [==============================] - 1s 9ms/step - loss: 0.9414 - accuracy: 0.6867 - recall: 0.5539 - precision: 0.7887 - AUROC: 0.9489 - AUPRC: 0.7561 - f1_score: 0.6507 - balanced_accuracy: 0.7687 - specificity: 0.9835 - miss_rate: 0.4461 - fall_out: 0.0165 - mcc: 0.6309 - val_loss: 0.7385 - val_accuracy: 0.7460 - val_recall: 0.6253 - val_precision: 0.8595 - val_AUROC: 0.9704 - val_AUPRC: 0.8409 - val_f1_score: 0.7239 - val_balanced_accuracy: 0.8069 - val_specificity: 0.9886 - val_miss_rate: 0.3747 - val_fall_out: 0.0114 - val_mcc: 0.7091
Epoch 11/100
63/63 [==============================] - 1s 10ms/step - loss: 0.8884 - accuracy: 0.7058 - recall: 0.5769 - precision: 0.8057 - AUROC: 0.9547 - AUPRC: 0.7787 - f1_score: 0.6724 - balanced_accuracy: 0.7807 - specificity: 0.9845 - miss_rate: 0.4231 - fall_out: 0.0155 - mcc: 0.6533 - val_loss: 0.6952 - val_accuracy: 0.7645 - val_recall: 0.6668 - val_precision: 0.8682 - val_AUROC: 0.9736 - val_AUPRC: 0.8562 - val_f1_score: 0.7543 - val_balanced_accuracy: 0.8278 - val_specificity: 0.9888 - val_miss_rate: 0.3332 - val_fall_out: 0.0112 - val_mcc: 0.7386
Epoch 12/100
63/63 [==============================] - 1s 10ms/step - loss: 0.8636 - accuracy: 0.7154 - recall: 0.5976 - precision: 0.8117 - AUROC: 0.9563 - AUPRC: 0.7889 - f1_score: 0.6884 - balanced_accuracy: 0.7911 - specificity: 0.9846 - miss_rate: 0.4024 - fall_out: 0.0154 - mcc: 0.6688 - val_loss: 0.6738 - val_accuracy: 0.7851 - val_recall: 0.6784 - val_precision: 0.8696 - val_AUROC: 0.9750 - val_AUPRC: 0.8675 - val_f1_score: 0.7622 - val_balanced_accuracy: 0.8335 - val_specificity: 0.9887 - val_miss_rate: 0.3216 - val_fall_out: 0.0113 - val_mcc: 0.7462
Epoch 13/100
63/63 [==============================] - 1s 10ms/step - loss: 0.8181 - accuracy: 0.7318 - recall: 0.6254 - precision: 0.8232 - AUROC: 0.9610 - AUPRC: 0.8059 - f1_score: 0.7108 - balanced_accuracy: 0.8052 - specificity: 0.9851 - miss_rate: 0.3746 - fall_out: 0.0149 - mcc: 0.6912 - val_loss: 0.6383 - val_accuracy: 0.7861 - val_recall: 0.6999 - val_precision: 0.8759 - val_AUROC: 0.9776 - val_AUPRC: 0.8785 - val_f1_score: 0.7781 - val_balanced_accuracy: 0.8444 - val_specificity: 0.9890 - val_miss_rate: 0.3001 - val_fall_out: 0.0110 - val_mcc: 0.7622
Epoch 14/100
63/63 [==============================] - 1s 10ms/step - loss: 0.7864 - accuracy: 0.7425 - recall: 0.6424 - precision: 0.8321 - AUROC: 0.9637 - AUPRC: 0.8188 - f1_score: 0.7250 - balanced_accuracy: 0.8140 - specificity: 0.9856 - miss_rate: 0.3576 - fall_out: 0.0144 - mcc: 0.7058 - val_loss: 0.6223 - val_accuracy: 0.7881 - val_recall: 0.7134 - val_precision: 0.8630 - val_AUROC: 0.9778 - val_AUPRC: 0.8788 - val_f1_score: 0.7811 - val_balanced_accuracy: 0.8504 - val_specificity: 0.9874 - val_miss_rate: 0.2866 - val_fall_out: 0.0126 - val_mcc: 0.7635
Epoch 15/100
63/63 [==============================] - 1s 10ms/step - loss: 0.7702 - accuracy: 0.7500 - recall: 0.6523 - precision: 0.8317 - AUROC: 0.9641 - AUPRC: 0.8233 - f1_score: 0.7312 - balanced_accuracy: 0.8188 - specificity: 0.9853 - miss_rate: 0.3477 - fall_out: 0.0147 - mcc: 0.7115 - val_loss: 0.5955 - val_accuracy: 0.7966 - val_recall: 0.7270 - val_precision: 0.8752 - val_AUROC: 0.9801 - val_AUPRC: 0.8895 - val_f1_score: 0.7942 - val_balanced_accuracy: 0.8577 - val_specificity: 0.9885 - val_miss_rate: 0.2730 - val_fall_out: 0.0115 - val_mcc: 0.7777
Epoch 16/100
63/63 [==============================] - 1s 10ms/step - loss: 0.7321 - accuracy: 0.7610 - recall: 0.6737 - precision: 0.8376 - AUROC: 0.9678 - AUPRC: 0.8397 - f1_score: 0.7468 - balanced_accuracy: 0.8296 - specificity: 0.9855 - miss_rate: 0.3263 - fall_out: 0.0145 - mcc: 0.7272 - val_loss: 0.5787 - val_accuracy: 0.7951 - val_recall: 0.7270 - val_precision: 0.8746 - val_AUROC: 0.9812 - val_AUPRC: 0.8945 - val_f1_score: 0.7940 - val_balanced_accuracy: 0.8577 - val_specificity: 0.9884 - val_miss_rate: 0.2730 - val_fall_out: 0.0116 - val_mcc: 0.7774
Epoch 17/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6755 - accuracy: 0.7747 - recall: 0.6951 - precision: 0.8484 - AUROC: 0.9724 - AUPRC: 0.8574 - f1_score: 0.7641 - balanced_accuracy: 0.8407 - specificity: 0.9862 - miss_rate: 0.3049 - fall_out: 0.0138 - mcc: 0.7452 - val_loss: 0.5492 - val_accuracy: 0.8076 - val_recall: 0.7525 - val_precision: 0.8748 - val_AUROC: 0.9825 - val_AUPRC: 0.9017 - val_f1_score: 0.8090 - val_balanced_accuracy: 0.8703 - val_specificity: 0.9880 - val_miss_rate: 0.2475 - val_fall_out: 0.0120 - val_mcc: 0.7923
Epoch 18/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6635 - accuracy: 0.7834 - recall: 0.7057 - precision: 0.8496 - AUROC: 0.9733 - AUPRC: 0.8613 - f1_score: 0.7710 - balanced_accuracy: 0.8459 - specificity: 0.9861 - miss_rate: 0.2943 - fall_out: 0.0139 - mcc: 0.7520 - val_loss: 0.5624 - val_accuracy: 0.8101 - val_recall: 0.7555 - val_precision: 0.8717 - val_AUROC: 0.9813 - val_AUPRC: 0.8966 - val_f1_score: 0.8094 - val_balanced_accuracy: 0.8716 - val_specificity: 0.9876 - val_miss_rate: 0.2445 - val_fall_out: 0.0124 - val_mcc: 0.7924
Epoch 19/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6534 - accuracy: 0.7852 - recall: 0.7154 - precision: 0.8484 - AUROC: 0.9740 - AUPRC: 0.8644 - f1_score: 0.7762 - balanced_accuracy: 0.8506 - specificity: 0.9858 - miss_rate: 0.2846 - fall_out: 0.0142 - mcc: 0.7570 - val_loss: 0.5333 - val_accuracy: 0.8141 - val_recall: 0.7625 - val_precision: 0.8707 - val_AUROC: 0.9836 - val_AUPRC: 0.9069 - val_f1_score: 0.8130 - val_balanced_accuracy: 0.8750 - val_specificity: 0.9874 - val_miss_rate: 0.2375 - val_fall_out: 0.0126 - val_mcc: 0.7959
Epoch 20/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6460 - accuracy: 0.7906 - recall: 0.7197 - precision: 0.8523 - AUROC: 0.9743 - AUPRC: 0.8667 - f1_score: 0.7804 - balanced_accuracy: 0.8529 - specificity: 0.9861 - miss_rate: 0.2803 - fall_out: 0.0139 - mcc: 0.7615 - val_loss: 0.5221 - val_accuracy: 0.8171 - val_recall: 0.7725 - val_precision: 0.8766 - val_AUROC: 0.9841 - val_AUPRC: 0.9100 - val_f1_score: 0.8213 - val_balanced_accuracy: 0.8802 - val_specificity: 0.9879 - val_miss_rate: 0.2275 - val_fall_out: 0.0121 - val_mcc: 0.8048
Epoch 21/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6113 - accuracy: 0.8055 - recall: 0.7420 - precision: 0.8651 - AUROC: 0.9764 - AUPRC: 0.8789 - f1_score: 0.7988 - balanced_accuracy: 0.8646 - specificity: 0.9871 - miss_rate: 0.2580 - fall_out: 0.0129 - mcc: 0.7811 - val_loss: 0.5148 - val_accuracy: 0.8246 - val_recall: 0.7771 - val_precision: 0.8743 - val_AUROC: 0.9845 - val_AUPRC: 0.9131 - val_f1_score: 0.8228 - val_balanced_accuracy: 0.8823 - val_specificity: 0.9876 - val_miss_rate: 0.2229 - val_fall_out: 0.0124 - val_mcc: 0.8061
Epoch 22/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5838 - accuracy: 0.8089 - recall: 0.7477 - precision: 0.8687 - AUROC: 0.9785 - AUPRC: 0.8884 - f1_score: 0.8037 - balanced_accuracy: 0.8676 - specificity: 0.9874 - miss_rate: 0.2523 - fall_out: 0.0126 - mcc: 0.7864 - val_loss: 0.5035 - val_accuracy: 0.8317 - val_recall: 0.7821 - val_precision: 0.8789 - val_AUROC: 0.9847 - val_AUPRC: 0.9152 - val_f1_score: 0.8277 - val_balanced_accuracy: 0.8850 - val_specificity: 0.9880 - val_miss_rate: 0.2179 - val_fall_out: 0.0120 - val_mcc: 0.8114
Epoch 23/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5689 - accuracy: 0.8109 - recall: 0.7568 - precision: 0.8629 - AUROC: 0.9799 - AUPRC: 0.8906 - f1_score: 0.8064 - balanced_accuracy: 0.8717 - specificity: 0.9866 - miss_rate: 0.2432 - fall_out: 0.0134 - mcc: 0.7885 - val_loss: 0.5004 - val_accuracy: 0.8206 - val_recall: 0.7866 - val_precision: 0.8835 - val_AUROC: 0.9848 - val_AUPRC: 0.9160 - val_f1_score: 0.8322 - val_balanced_accuracy: 0.8875 - val_specificity: 0.9885 - val_miss_rate: 0.2134 - val_fall_out: 0.0115 - val_mcc: 0.8165
Epoch 24/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5462 - accuracy: 0.8194 - recall: 0.7642 - precision: 0.8724 - AUROC: 0.9810 - AUPRC: 0.8992 - f1_score: 0.8147 - balanced_accuracy: 0.8759 - specificity: 0.9876 - miss_rate: 0.2358 - fall_out: 0.0124 - mcc: 0.7978 - val_loss: 0.4832 - val_accuracy: 0.8357 - val_recall: 0.7981 - val_precision: 0.8875 - val_AUROC: 0.9857 - val_AUPRC: 0.9207 - val_f1_score: 0.8404 - val_balanced_accuracy: 0.8934 - val_specificity: 0.9888 - val_miss_rate: 0.2019 - val_fall_out: 0.0112 - val_mcc: 0.8251
Epoch 25/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5501 - accuracy: 0.8223 - recall: 0.7679 - precision: 0.8735 - AUROC: 0.9807 - AUPRC: 0.8974 - f1_score: 0.8173 - balanced_accuracy: 0.8778 - specificity: 0.9876 - miss_rate: 0.2321 - fall_out: 0.0124 - mcc: 0.8005 - val_loss: 0.4776 - val_accuracy: 0.8387 - val_recall: 0.8031 - val_precision: 0.8911 - val_AUROC: 0.9855 - val_AUPRC: 0.9231 - val_f1_score: 0.8448 - val_balanced_accuracy: 0.8961 - val_specificity: 0.9891 - val_miss_rate: 0.1969 - val_fall_out: 0.0109 - val_mcc: 0.8299
Epoch 26/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5221 - accuracy: 0.8320 - recall: 0.7848 - precision: 0.8780 - AUROC: 0.9820 - AUPRC: 0.9043 - f1_score: 0.8288 - balanced_accuracy: 0.8863 - specificity: 0.9879 - miss_rate: 0.2152 - fall_out: 0.0121 - mcc: 0.8125 - val_loss: 0.4743 - val_accuracy: 0.8442 - val_recall: 0.8131 - val_precision: 0.8883 - val_AUROC: 0.9861 - val_AUPRC: 0.9231 - val_f1_score: 0.8491 - val_balanced_accuracy: 0.9009 - val_specificity: 0.9886 - val_miss_rate: 0.1869 - val_fall_out: 0.0114 - val_mcc: 0.8341
Epoch 27/100
63/63 [==============================] - 1s 9ms/step - loss: 0.5187 - accuracy: 0.8344 - recall: 0.7874 - precision: 0.8781 - AUROC: 0.9818 - AUPRC: 0.9072 - f1_score: 0.8303 - balanced_accuracy: 0.8877 - specificity: 0.9879 - miss_rate: 0.2126 - fall_out: 0.0121 - mcc: 0.8140 - val_loss: 0.4692 - val_accuracy: 0.8422 - val_recall: 0.8071 - val_precision: 0.8813 - val_AUROC: 0.9859 - val_AUPRC: 0.9244 - val_f1_score: 0.8426 - val_balanced_accuracy: 0.8975 - val_specificity: 0.9879 - val_miss_rate: 0.1929 - val_fall_out: 0.0121 - val_mcc: 0.8269
Epoch 28/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5017 - accuracy: 0.8382 - recall: 0.7927 - precision: 0.8832 - AUROC: 0.9833 - AUPRC: 0.9112 - f1_score: 0.8355 - balanced_accuracy: 0.8905 - specificity: 0.9884 - miss_rate: 0.2073 - fall_out: 0.0116 - mcc: 0.8198 - val_loss: 0.4629 - val_accuracy: 0.8482 - val_recall: 0.8191 - val_precision: 0.8871 - val_AUROC: 0.9867 - val_AUPRC: 0.9271 - val_f1_score: 0.8518 - val_balanced_accuracy: 0.9038 - val_specificity: 0.9884 - val_miss_rate: 0.1809 - val_fall_out: 0.0116 - val_mcc: 0.8369
Epoch 29/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4904 - accuracy: 0.8399 - recall: 0.7960 - precision: 0.8818 - AUROC: 0.9840 - AUPRC: 0.9139 - f1_score: 0.8367 - balanced_accuracy: 0.8921 - specificity: 0.9881 - miss_rate: 0.2040 - fall_out: 0.0119 - mcc: 0.8209 - val_loss: 0.4435 - val_accuracy: 0.8527 - val_recall: 0.8136 - val_precision: 0.8904 - val_AUROC: 0.9885 - val_AUPRC: 0.9319 - val_f1_score: 0.8503 - val_balanced_accuracy: 0.9012 - val_specificity: 0.9889 - val_miss_rate: 0.1864 - val_fall_out: 0.0111 - val_mcc: 0.8355
Epoch 30/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4765 - accuracy: 0.8439 - recall: 0.8035 - precision: 0.8862 - AUROC: 0.9849 - AUPRC: 0.9185 - f1_score: 0.8428 - balanced_accuracy: 0.8960 - specificity: 0.9885 - miss_rate: 0.1965 - fall_out: 0.0115 - mcc: 0.8275 - val_loss: 0.4397 - val_accuracy: 0.8512 - val_recall: 0.8262 - val_precision: 0.8870 - val_AUROC: 0.9882 - val_AUPRC: 0.9323 - val_f1_score: 0.8555 - val_balanced_accuracy: 0.9072 - val_specificity: 0.9883 - val_miss_rate: 0.1738 - val_fall_out: 0.0117 - val_mcc: 0.8407
Epoch 31/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4647 - accuracy: 0.8562 - recall: 0.8138 - precision: 0.8920 - AUROC: 0.9849 - AUPRC: 0.9221 - f1_score: 0.8511 - balanced_accuracy: 0.9014 - specificity: 0.9890 - miss_rate: 0.1862 - fall_out: 0.0110 - mcc: 0.8364 - val_loss: 0.4386 - val_accuracy: 0.8587 - val_recall: 0.8246 - val_precision: 0.8926 - val_AUROC: 0.9876 - val_AUPRC: 0.9323 - val_f1_score: 0.8573 - val_balanced_accuracy: 0.9068 - val_specificity: 0.9890 - val_miss_rate: 0.1754 - val_fall_out: 0.0110 - val_mcc: 0.8429
Epoch 32/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4683 - accuracy: 0.8478 - recall: 0.8105 - precision: 0.8914 - AUROC: 0.9847 - AUPRC: 0.9219 - f1_score: 0.8490 - balanced_accuracy: 0.8998 - specificity: 0.9890 - miss_rate: 0.1895 - fall_out: 0.0110 - mcc: 0.8343 - val_loss: 0.4450 - val_accuracy: 0.8562 - val_recall: 0.8246 - val_precision: 0.8821 - val_AUROC: 0.9874 - val_AUPRC: 0.9306 - val_f1_score: 0.8524 - val_balanced_accuracy: 0.9062 - val_specificity: 0.9878 - val_miss_rate: 0.1754 - val_fall_out: 0.0122 - val_mcc: 0.8372
Epoch 33/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4478 - accuracy: 0.8542 - recall: 0.8158 - precision: 0.8927 - AUROC: 0.9862 - AUPRC: 0.9276 - f1_score: 0.8525 - balanced_accuracy: 0.9024 - specificity: 0.9891 - miss_rate: 0.1842 - fall_out: 0.0109 - mcc: 0.8380 - val_loss: 0.4266 - val_accuracy: 0.8597 - val_recall: 0.8337 - val_precision: 0.8985 - val_AUROC: 0.9882 - val_AUPRC: 0.9356 - val_f1_score: 0.8649 - val_balanced_accuracy: 0.9116 - val_specificity: 0.9895 - val_miss_rate: 0.1663 - val_fall_out: 0.0105 - val_mcc: 0.8512
Epoch 34/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4265 - accuracy: 0.8595 - recall: 0.8244 - precision: 0.8949 - AUROC: 0.9874 - AUPRC: 0.9320 - f1_score: 0.8582 - balanced_accuracy: 0.9068 - specificity: 0.9892 - miss_rate: 0.1756 - fall_out: 0.0108 - mcc: 0.8440 - val_loss: 0.4232 - val_accuracy: 0.8692 - val_recall: 0.8427 - val_precision: 0.8980 - val_AUROC: 0.9879 - val_AUPRC: 0.9374 - val_f1_score: 0.8695 - val_balanced_accuracy: 0.9160 - val_specificity: 0.9894 - val_miss_rate: 0.1573 - val_fall_out: 0.0106 - val_mcc: 0.8560
Epoch 35/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4024 - accuracy: 0.8745 - recall: 0.8414 - precision: 0.9064 - AUROC: 0.9885 - AUPRC: 0.9373 - f1_score: 0.8727 - balanced_accuracy: 0.9159 - specificity: 0.9903 - miss_rate: 0.1586 - fall_out: 0.0097 - mcc: 0.8599 - val_loss: 0.4151 - val_accuracy: 0.8672 - val_recall: 0.8477 - val_precision: 0.8924 - val_AUROC: 0.9880 - val_AUPRC: 0.9388 - val_f1_score: 0.8695 - val_balanced_accuracy: 0.9182 - val_specificity: 0.9886 - val_miss_rate: 0.1523 - val_fall_out: 0.0114 - val_mcc: 0.8557
Epoch 36/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4070 - accuracy: 0.8679 - recall: 0.8372 - precision: 0.9011 - AUROC: 0.9886 - AUPRC: 0.9382 - f1_score: 0.8679 - balanced_accuracy: 0.9135 - specificity: 0.9898 - miss_rate: 0.1628 - fall_out: 0.0102 - mcc: 0.8546 - val_loss: 0.4201 - val_accuracy: 0.8657 - val_recall: 0.8462 - val_precision: 0.8965 - val_AUROC: 0.9873 - val_AUPRC: 0.9381 - val_f1_score: 0.8706 - val_balanced_accuracy: 0.9177 - val_specificity: 0.9891 - val_miss_rate: 0.1538 - val_fall_out: 0.0109 - val_mcc: 0.8571
Epoch 37/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4048 - accuracy: 0.8690 - recall: 0.8388 - precision: 0.9006 - AUROC: 0.9883 - AUPRC: 0.9369 - f1_score: 0.8686 - balanced_accuracy: 0.9143 - specificity: 0.9897 - miss_rate: 0.1612 - fall_out: 0.0103 - mcc: 0.8552 - val_loss: 0.4041 - val_accuracy: 0.8657 - val_recall: 0.8407 - val_precision: 0.8973 - val_AUROC: 0.9893 - val_AUPRC: 0.9420 - val_f1_score: 0.8681 - val_balanced_accuracy: 0.9150 - val_specificity: 0.9893 - val_miss_rate: 0.1593 - val_fall_out: 0.0107 - val_mcc: 0.8545
Epoch 38/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3758 - accuracy: 0.8800 - recall: 0.8498 - precision: 0.9084 - AUROC: 0.9899 - AUPRC: 0.9451 - f1_score: 0.8781 - balanced_accuracy: 0.9202 - specificity: 0.9905 - miss_rate: 0.1502 - fall_out: 0.0095 - mcc: 0.8657 - val_loss: 0.3913 - val_accuracy: 0.8763 - val_recall: 0.8547 - val_precision: 0.9007 - val_AUROC: 0.9891 - val_AUPRC: 0.9431 - val_f1_score: 0.8771 - val_balanced_accuracy: 0.9221 - val_specificity: 0.9895 - val_miss_rate: 0.1453 - val_fall_out: 0.0105 - val_mcc: 0.8642
Epoch 39/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3784 - accuracy: 0.8776 - recall: 0.8473 - precision: 0.9047 - AUROC: 0.9895 - AUPRC: 0.9441 - f1_score: 0.8750 - balanced_accuracy: 0.9187 - specificity: 0.9901 - miss_rate: 0.1527 - fall_out: 0.0099 - mcc: 0.8622 - val_loss: 0.3862 - val_accuracy: 0.8798 - val_recall: 0.8607 - val_precision: 0.9033 - val_AUROC: 0.9882 - val_AUPRC: 0.9457 - val_f1_score: 0.8815 - val_balanced_accuracy: 0.9252 - val_specificity: 0.9898 - val_miss_rate: 0.1393 - val_fall_out: 0.0102 - val_mcc: 0.8690
Epoch 40/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3704 - accuracy: 0.8805 - recall: 0.8527 - precision: 0.9089 - AUROC: 0.9900 - AUPRC: 0.9452 - f1_score: 0.8799 - balanced_accuracy: 0.9216 - specificity: 0.9905 - miss_rate: 0.1473 - fall_out: 0.0095 - mcc: 0.8676 - val_loss: 0.3888 - val_accuracy: 0.8763 - val_recall: 0.8537 - val_precision: 0.9035 - val_AUROC: 0.9900 - val_AUPRC: 0.9464 - val_f1_score: 0.8779 - val_balanced_accuracy: 0.9218 - val_specificity: 0.9899 - val_miss_rate: 0.1463 - val_fall_out: 0.0101 - val_mcc: 0.8652
Epoch 41/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3568 - accuracy: 0.8811 - recall: 0.8551 - precision: 0.9082 - AUROC: 0.9911 - AUPRC: 0.9501 - f1_score: 0.8808 - balanced_accuracy: 0.9227 - specificity: 0.9904 - miss_rate: 0.1449 - fall_out: 0.0096 - mcc: 0.8685 - val_loss: 0.3765 - val_accuracy: 0.8813 - val_recall: 0.8607 - val_precision: 0.9042 - val_AUROC: 0.9903 - val_AUPRC: 0.9483 - val_f1_score: 0.8819 - val_balanced_accuracy: 0.9253 - val_specificity: 0.9899 - val_miss_rate: 0.1393 - val_fall_out: 0.0101 - val_mcc: 0.8695
Epoch 42/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3439 - accuracy: 0.8900 - recall: 0.8672 - precision: 0.9153 - AUROC: 0.9914 - AUPRC: 0.9525 - f1_score: 0.8906 - balanced_accuracy: 0.9292 - specificity: 0.9911 - miss_rate: 0.1328 - fall_out: 0.0089 - mcc: 0.8792 - val_loss: 0.3801 - val_accuracy: 0.8763 - val_recall: 0.8577 - val_precision: 0.9015 - val_AUROC: 0.9899 - val_AUPRC: 0.9483 - val_f1_score: 0.8791 - val_balanced_accuracy: 0.9237 - val_specificity: 0.9896 - val_miss_rate: 0.1423 - val_fall_out: 0.0104 - val_mcc: 0.8663
Epoch 43/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3550 - accuracy: 0.8874 - recall: 0.8642 - precision: 0.9126 - AUROC: 0.9908 - AUPRC: 0.9498 - f1_score: 0.8877 - balanced_accuracy: 0.9275 - specificity: 0.9908 - miss_rate: 0.1358 - fall_out: 0.0092 - mcc: 0.8760 - val_loss: 0.3646 - val_accuracy: 0.8808 - val_recall: 0.8602 - val_precision: 0.9080 - val_AUROC: 0.9912 - val_AUPRC: 0.9497 - val_f1_score: 0.8835 - val_balanced_accuracy: 0.9253 - val_specificity: 0.9903 - val_miss_rate: 0.1398 - val_fall_out: 0.0097 - val_mcc: 0.8713
Epoch 44/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3444 - accuracy: 0.8883 - recall: 0.8659 - precision: 0.9131 - AUROC: 0.9914 - AUPRC: 0.9518 - f1_score: 0.8888 - balanced_accuracy: 0.9283 - specificity: 0.9908 - miss_rate: 0.1341 - fall_out: 0.0092 - mcc: 0.8772 - val_loss: 0.3831 - val_accuracy: 0.8763 - val_recall: 0.8592 - val_precision: 0.9012 - val_AUROC: 0.9890 - val_AUPRC: 0.9444 - val_f1_score: 0.8797 - val_balanced_accuracy: 0.9244 - val_specificity: 0.9895 - val_miss_rate: 0.1408 - val_fall_out: 0.0105 - val_mcc: 0.8670
Epoch 45/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3462 - accuracy: 0.8914 - recall: 0.8694 - precision: 0.9155 - AUROC: 0.9908 - AUPRC: 0.9518 - f1_score: 0.8918 - balanced_accuracy: 0.9302 - specificity: 0.9911 - miss_rate: 0.1306 - fall_out: 0.0089 - mcc: 0.8805 - val_loss: 0.3687 - val_accuracy: 0.8833 - val_recall: 0.8667 - val_precision: 0.9043 - val_AUROC: 0.9898 - val_AUPRC: 0.9491 - val_f1_score: 0.8851 - val_balanced_accuracy: 0.9283 - val_specificity: 0.9898 - val_miss_rate: 0.1333 - val_fall_out: 0.0102 - val_mcc: 0.8729
250/250 [==============================] - 1s 5ms/step - loss: 0.1091 - accuracy: 0.9694 - recall: 0.9617 - precision: 0.9806 - AUROC: 0.9993 - AUPRC: 0.9952 - f1_score: 0.9710 - balanced_accuracy: 0.9798 - specificity: 0.9979 - miss_rate: 0.0383 - fall_out: 0.0021 - mcc: 0.9679
63/63 [==============================] - 0s 5ms/step - loss: 0.3687 - accuracy: 0.8833 - recall: 0.8667 - precision: 0.9043 - AUROC: 0.9898 - AUPRC: 0.9491 - f1_score: 0.8851 - balanced_accuracy: 0.9283 - specificity: 0.9898 - miss_rate: 0.1333 - fall_out: 0.0102 - mcc: 0.8729
1it [00:35, 35.04s/it]
-- HOLDOUT 2 -- WINDOW window_3s
-- 6 Uncorrelated features: [Pearson+Spearman]
['tempo', 'mfcc16_mean', 'mfcc10_var', 'mfcc19_mean', 'mfcc11_var', 'mfcc17_mean']
-- Actually uncorrelated features: [confirmed via MIC]
[]
-- 1 Correlated features: [Pearson]
['spectral_centroid_mean']
Model: "MLP"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_225 (Dense) (None, 256) 14592
dropout_174 (Dropout) (None, 256) 0
dense_226 (Dense) (None, 256) 65792
dropout_175 (Dropout) (None, 256) 0
dense_227 (Dense) (None, 128) 32896
dropout_176 (Dropout) (None, 128) 0
dense_228 (Dense) (None, 128) 16512
dropout_177 (Dropout) (None, 128) 0
dense_229 (Dense) (None, 10) 1290
=================================================================
Total params: 131,082
Trainable params: 131,082
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 2s 18ms/step - loss: 2.2140 - accuracy: 0.2167 - recall: 0.0266 - precision: 0.4680 - AUROC: 0.6568 - AUPRC: 0.1974 - f1_score: 0.0503 - balanced_accuracy: 0.5116 - specificity: 0.9966 - miss_rate: 0.9734 - fall_out: 0.0034 - mcc: 0.0927 - val_loss: 1.7024 - val_accuracy: 0.4243 - val_recall: 0.1253 - val_precision: 0.8306 - val_AUROC: 0.8482 - val_AUPRC: 0.4501 - val_f1_score: 0.2177 - val_balanced_accuracy: 0.5612 - val_specificity: 0.9972 - val_miss_rate: 0.8747 - val_fall_out: 0.0028 - val_mcc: 0.3013
Epoch 2/100
63/63 [==============================] - 1s 10ms/step - loss: 1.7678 - accuracy: 0.3720 - recall: 0.1556 - precision: 0.6254 - AUROC: 0.8183 - AUPRC: 0.3827 - f1_score: 0.2491 - balanced_accuracy: 0.5726 - specificity: 0.9896 - miss_rate: 0.8444 - fall_out: 0.0104 - mcc: 0.2797 - val_loss: 1.4344 - val_accuracy: 0.4865 - val_recall: 0.2440 - val_precision: 0.7681 - val_AUROC: 0.8940 - val_AUPRC: 0.5512 - val_f1_score: 0.3703 - val_balanced_accuracy: 0.6179 - val_specificity: 0.9918 - val_miss_rate: 0.7560 - val_fall_out: 0.0082 - val_mcc: 0.4034
Epoch 3/100
63/63 [==============================] - 1s 9ms/step - loss: 1.5550 - accuracy: 0.4554 - recall: 0.2238 - precision: 0.6723 - AUROC: 0.8645 - AUPRC: 0.4775 - f1_score: 0.3358 - balanced_accuracy: 0.6059 - specificity: 0.9879 - miss_rate: 0.7762 - fall_out: 0.0121 - mcc: 0.3540 - val_loss: 1.2465 - val_accuracy: 0.5681 - val_recall: 0.3081 - val_precision: 0.7905 - val_AUROC: 0.9212 - val_AUPRC: 0.6338 - val_f1_score: 0.4434 - val_balanced_accuracy: 0.6495 - val_specificity: 0.9909 - val_miss_rate: 0.6919 - val_fall_out: 0.0091 - val_mcc: 0.4635
Epoch 4/100
63/63 [==============================] - 1s 9ms/step - loss: 1.3989 - accuracy: 0.5085 - recall: 0.2962 - precision: 0.7032 - AUROC: 0.8916 - AUPRC: 0.5495 - f1_score: 0.4169 - balanced_accuracy: 0.6412 - specificity: 0.9861 - miss_rate: 0.7038 - fall_out: 0.0139 - mcc: 0.4217 - val_loss: 1.1206 - val_accuracy: 0.6222 - val_recall: 0.3863 - val_precision: 0.7948 - val_AUROC: 0.9338 - val_AUPRC: 0.6863 - val_f1_score: 0.5199 - val_balanced_accuracy: 0.6876 - val_specificity: 0.9889 - val_miss_rate: 0.6137 - val_fall_out: 0.0111 - val_mcc: 0.5235
Epoch 5/100
63/63 [==============================] - 1s 10ms/step - loss: 1.2822 - accuracy: 0.5555 - recall: 0.3546 - precision: 0.7283 - AUROC: 0.9089 - AUPRC: 0.5999 - f1_score: 0.4770 - balanced_accuracy: 0.6699 - specificity: 0.9853 - miss_rate: 0.6454 - fall_out: 0.0147 - mcc: 0.4738 - val_loss: 1.0283 - val_accuracy: 0.6518 - val_recall: 0.4359 - val_precision: 0.8215 - val_AUROC: 0.9449 - val_AUPRC: 0.7280 - val_f1_score: 0.5696 - val_balanced_accuracy: 0.7127 - val_specificity: 0.9895 - val_miss_rate: 0.5641 - val_fall_out: 0.0105 - val_mcc: 0.5693
Epoch 6/100
63/63 [==============================] - 1s 9ms/step - loss: 1.1853 - accuracy: 0.5922 - recall: 0.4112 - precision: 0.7485 - AUROC: 0.9214 - AUPRC: 0.6503 - f1_score: 0.5308 - balanced_accuracy: 0.6979 - specificity: 0.9846 - miss_rate: 0.5888 - fall_out: 0.0154 - mcc: 0.5212 - val_loss: 0.9486 - val_accuracy: 0.6779 - val_recall: 0.4820 - val_precision: 0.8258 - val_AUROC: 0.9530 - val_AUPRC: 0.7599 - val_f1_score: 0.6087 - val_balanced_accuracy: 0.7353 - val_specificity: 0.9887 - val_miss_rate: 0.5180 - val_fall_out: 0.0113 - val_mcc: 0.6023
Epoch 7/100
63/63 [==============================] - 1s 10ms/step - loss: 1.0938 - accuracy: 0.6278 - recall: 0.4626 - precision: 0.7662 - AUROC: 0.9329 - AUPRC: 0.6896 - f1_score: 0.5769 - balanced_accuracy: 0.7234 - specificity: 0.9843 - miss_rate: 0.5374 - fall_out: 0.0157 - mcc: 0.5629 - val_loss: 0.8779 - val_accuracy: 0.7114 - val_recall: 0.5481 - val_precision: 0.8364 - val_AUROC: 0.9579 - val_AUPRC: 0.7857 - val_f1_score: 0.6622 - val_balanced_accuracy: 0.7681 - val_specificity: 0.9881 - val_miss_rate: 0.4519 - val_fall_out: 0.0119 - val_mcc: 0.6500
Epoch 8/100
63/63 [==============================] - 1s 10ms/step - loss: 1.0391 - accuracy: 0.6498 - recall: 0.4858 - precision: 0.7803 - AUROC: 0.9392 - AUPRC: 0.7165 - f1_score: 0.5988 - balanced_accuracy: 0.7353 - specificity: 0.9848 - miss_rate: 0.5142 - fall_out: 0.0152 - mcc: 0.5843 - val_loss: 0.8375 - val_accuracy: 0.7204 - val_recall: 0.5912 - val_precision: 0.8363 - val_AUROC: 0.9609 - val_AUPRC: 0.8019 - val_f1_score: 0.6927 - val_balanced_accuracy: 0.7892 - val_specificity: 0.9871 - val_miss_rate: 0.4088 - val_fall_out: 0.0129 - val_mcc: 0.6769
Epoch 9/100
63/63 [==============================] - 1s 10ms/step - loss: 0.9878 - accuracy: 0.6705 - recall: 0.5304 - precision: 0.7920 - AUROC: 0.9442 - AUPRC: 0.7376 - f1_score: 0.6354 - balanced_accuracy: 0.7575 - specificity: 0.9845 - miss_rate: 0.4696 - fall_out: 0.0155 - mcc: 0.6180 - val_loss: 0.7953 - val_accuracy: 0.7285 - val_recall: 0.5922 - val_precision: 0.8365 - val_AUROC: 0.9654 - val_AUPRC: 0.8139 - val_f1_score: 0.6935 - val_balanced_accuracy: 0.7897 - val_specificity: 0.9871 - val_miss_rate: 0.4078 - val_fall_out: 0.0129 - val_mcc: 0.6776
Epoch 10/100
63/63 [==============================] - 1s 10ms/step - loss: 0.9345 - accuracy: 0.6871 - recall: 0.5530 - precision: 0.7992 - AUROC: 0.9500 - AUPRC: 0.7580 - f1_score: 0.6537 - balanced_accuracy: 0.7688 - specificity: 0.9846 - miss_rate: 0.4470 - fall_out: 0.0154 - mcc: 0.6355 - val_loss: 0.7418 - val_accuracy: 0.7475 - val_recall: 0.6423 - val_precision: 0.8451 - val_AUROC: 0.9692 - val_AUPRC: 0.8354 - val_f1_score: 0.7299 - val_balanced_accuracy: 0.8146 - val_specificity: 0.9869 - val_miss_rate: 0.3577 - val_fall_out: 0.0131 - val_mcc: 0.7123
Epoch 11/100
63/63 [==============================] - 1s 10ms/step - loss: 0.8890 - accuracy: 0.7115 - recall: 0.5891 - precision: 0.8125 - AUROC: 0.9541 - AUPRC: 0.7787 - f1_score: 0.6830 - balanced_accuracy: 0.7870 - specificity: 0.9849 - miss_rate: 0.4109 - fall_out: 0.0151 - mcc: 0.6640 - val_loss: 0.7238 - val_accuracy: 0.7580 - val_recall: 0.6473 - val_precision: 0.8450 - val_AUROC: 0.9708 - val_AUPRC: 0.8405 - val_f1_score: 0.7330 - val_balanced_accuracy: 0.8171 - val_specificity: 0.9868 - val_miss_rate: 0.3527 - val_fall_out: 0.0132 - val_mcc: 0.7153
Epoch 12/100
63/63 [==============================] - 1s 10ms/step - loss: 0.8500 - accuracy: 0.7188 - recall: 0.6023 - precision: 0.8115 - AUROC: 0.9581 - AUPRC: 0.7929 - f1_score: 0.6914 - balanced_accuracy: 0.7934 - specificity: 0.9845 - miss_rate: 0.3977 - fall_out: 0.0155 - mcc: 0.6715 - val_loss: 0.6941 - val_accuracy: 0.7665 - val_recall: 0.6698 - val_precision: 0.8631 - val_AUROC: 0.9734 - val_AUPRC: 0.8538 - val_f1_score: 0.7543 - val_balanced_accuracy: 0.8290 - val_specificity: 0.9882 - val_miss_rate: 0.3302 - val_fall_out: 0.0118 - val_mcc: 0.7379
Epoch 13/100
63/63 [==============================] - 1s 10ms/step - loss: 0.8036 - accuracy: 0.7330 - recall: 0.6270 - precision: 0.8257 - AUROC: 0.9625 - AUPRC: 0.8123 - f1_score: 0.7128 - balanced_accuracy: 0.8061 - specificity: 0.9853 - miss_rate: 0.3730 - fall_out: 0.0147 - mcc: 0.6934 - val_loss: 0.6557 - val_accuracy: 0.7806 - val_recall: 0.7019 - val_precision: 0.8590 - val_AUROC: 0.9757 - val_AUPRC: 0.8655 - val_f1_score: 0.7725 - val_balanced_accuracy: 0.8446 - val_specificity: 0.9872 - val_miss_rate: 0.2981 - val_fall_out: 0.0128 - val_mcc: 0.7547
Epoch 14/100
63/63 [==============================] - 1s 10ms/step - loss: 0.7917 - accuracy: 0.7410 - recall: 0.6418 - precision: 0.8257 - AUROC: 0.9630 - AUPRC: 0.8167 - f1_score: 0.7222 - balanced_accuracy: 0.8134 - specificity: 0.9849 - miss_rate: 0.3582 - fall_out: 0.0151 - mcc: 0.7022 - val_loss: 0.6190 - val_accuracy: 0.8056 - val_recall: 0.7119 - val_precision: 0.8772 - val_AUROC: 0.9783 - val_AUPRC: 0.8786 - val_f1_score: 0.7860 - val_balanced_accuracy: 0.8504 - val_specificity: 0.9889 - val_miss_rate: 0.2881 - val_fall_out: 0.0111 - val_mcc: 0.7699
Epoch 15/100
63/63 [==============================] - 1s 10ms/step - loss: 0.7341 - accuracy: 0.7558 - recall: 0.6657 - precision: 0.8383 - AUROC: 0.9680 - AUPRC: 0.8381 - f1_score: 0.7421 - balanced_accuracy: 0.8257 - specificity: 0.9857 - miss_rate: 0.3343 - fall_out: 0.0143 - mcc: 0.7228 - val_loss: 0.6096 - val_accuracy: 0.7941 - val_recall: 0.7335 - val_precision: 0.8683 - val_AUROC: 0.9778 - val_AUPRC: 0.8787 - val_f1_score: 0.7952 - val_balanced_accuracy: 0.8606 - val_specificity: 0.9876 - val_miss_rate: 0.2665 - val_fall_out: 0.0124 - val_mcc: 0.7779
Epoch 16/100
63/63 [==============================] - 1s 10ms/step - loss: 0.7367 - accuracy: 0.7601 - recall: 0.6717 - precision: 0.8338 - AUROC: 0.9672 - AUPRC: 0.8326 - f1_score: 0.7440 - balanced_accuracy: 0.8284 - specificity: 0.9851 - miss_rate: 0.3283 - fall_out: 0.0149 - mcc: 0.7240 - val_loss: 0.5836 - val_accuracy: 0.8001 - val_recall: 0.7330 - val_precision: 0.8682 - val_AUROC: 0.9802 - val_AUPRC: 0.8876 - val_f1_score: 0.7949 - val_balanced_accuracy: 0.8603 - val_specificity: 0.9876 - val_miss_rate: 0.2670 - val_fall_out: 0.0124 - val_mcc: 0.7776
Epoch 17/100
63/63 [==============================] - 1s 10ms/step - loss: 0.7048 - accuracy: 0.7717 - recall: 0.6905 - precision: 0.8462 - AUROC: 0.9702 - AUPRC: 0.8478 - f1_score: 0.7605 - balanced_accuracy: 0.8383 - specificity: 0.9861 - miss_rate: 0.3095 - fall_out: 0.0139 - mcc: 0.7414 - val_loss: 0.5702 - val_accuracy: 0.8086 - val_recall: 0.7430 - val_precision: 0.8780 - val_AUROC: 0.9814 - val_AUPRC: 0.8932 - val_f1_score: 0.8049 - val_balanced_accuracy: 0.8658 - val_specificity: 0.9885 - val_miss_rate: 0.2570 - val_fall_out: 0.0115 - val_mcc: 0.7885
Epoch 18/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6700 - accuracy: 0.7753 - recall: 0.6999 - precision: 0.8441 - AUROC: 0.9734 - AUPRC: 0.8574 - f1_score: 0.7653 - balanced_accuracy: 0.8428 - specificity: 0.9856 - miss_rate: 0.3001 - fall_out: 0.0144 - mcc: 0.7458 - val_loss: 0.5523 - val_accuracy: 0.8136 - val_recall: 0.7590 - val_precision: 0.8844 - val_AUROC: 0.9823 - val_AUPRC: 0.8998 - val_f1_score: 0.8169 - val_balanced_accuracy: 0.8740 - val_specificity: 0.9890 - val_miss_rate: 0.2410 - val_fall_out: 0.0110 - val_mcc: 0.8011
Epoch 19/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6460 - accuracy: 0.7892 - recall: 0.7141 - precision: 0.8540 - AUROC: 0.9747 - AUPRC: 0.8671 - f1_score: 0.7778 - balanced_accuracy: 0.8502 - specificity: 0.9864 - miss_rate: 0.2859 - fall_out: 0.0136 - mcc: 0.7592 - val_loss: 0.5477 - val_accuracy: 0.8196 - val_recall: 0.7625 - val_precision: 0.8717 - val_AUROC: 0.9821 - val_AUPRC: 0.8998 - val_f1_score: 0.8135 - val_balanced_accuracy: 0.8750 - val_specificity: 0.9875 - val_miss_rate: 0.2375 - val_fall_out: 0.0125 - val_mcc: 0.7964
Epoch 20/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6337 - accuracy: 0.7915 - recall: 0.7214 - precision: 0.8591 - AUROC: 0.9755 - AUPRC: 0.8709 - f1_score: 0.7843 - balanced_accuracy: 0.8541 - specificity: 0.9868 - miss_rate: 0.2786 - fall_out: 0.0132 - mcc: 0.7661 - val_loss: 0.5133 - val_accuracy: 0.8367 - val_recall: 0.7876 - val_precision: 0.8807 - val_AUROC: 0.9834 - val_AUPRC: 0.9093 - val_f1_score: 0.8315 - val_balanced_accuracy: 0.8879 - val_specificity: 0.9881 - val_miss_rate: 0.2124 - val_fall_out: 0.0119 - val_mcc: 0.8155
Epoch 21/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6001 - accuracy: 0.8069 - recall: 0.7459 - precision: 0.8612 - AUROC: 0.9773 - AUPRC: 0.8829 - f1_score: 0.7994 - balanced_accuracy: 0.8663 - specificity: 0.9866 - miss_rate: 0.2541 - fall_out: 0.0134 - mcc: 0.7813 - val_loss: 0.5024 - val_accuracy: 0.8362 - val_recall: 0.7846 - val_precision: 0.8893 - val_AUROC: 0.9844 - val_AUPRC: 0.9136 - val_f1_score: 0.8336 - val_balanced_accuracy: 0.8869 - val_specificity: 0.9891 - val_miss_rate: 0.2154 - val_fall_out: 0.0109 - val_mcc: 0.8184
Epoch 22/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5825 - accuracy: 0.8106 - recall: 0.7516 - precision: 0.8698 - AUROC: 0.9785 - AUPRC: 0.8872 - f1_score: 0.8064 - balanced_accuracy: 0.8696 - specificity: 0.9875 - miss_rate: 0.2484 - fall_out: 0.0125 - mcc: 0.7892 - val_loss: 0.4887 - val_accuracy: 0.8417 - val_recall: 0.7901 - val_precision: 0.8845 - val_AUROC: 0.9849 - val_AUPRC: 0.9164 - val_f1_score: 0.8346 - val_balanced_accuracy: 0.8893 - val_specificity: 0.9885 - val_miss_rate: 0.2099 - val_fall_out: 0.0115 - val_mcc: 0.8190
Epoch 23/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5673 - accuracy: 0.8153 - recall: 0.7576 - precision: 0.8710 - AUROC: 0.9795 - AUPRC: 0.8925 - f1_score: 0.8104 - balanced_accuracy: 0.8726 - specificity: 0.9875 - miss_rate: 0.2424 - fall_out: 0.0125 - mcc: 0.7933 - val_loss: 0.4847 - val_accuracy: 0.8362 - val_recall: 0.8006 - val_precision: 0.8858 - val_AUROC: 0.9850 - val_AUPRC: 0.9180 - val_f1_score: 0.8411 - val_balanced_accuracy: 0.8946 - val_specificity: 0.9885 - val_miss_rate: 0.1994 - val_fall_out: 0.0115 - val_mcc: 0.8257
Epoch 24/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5507 - accuracy: 0.8185 - recall: 0.7649 - precision: 0.8716 - AUROC: 0.9807 - AUPRC: 0.8975 - f1_score: 0.8148 - balanced_accuracy: 0.8762 - specificity: 0.9875 - miss_rate: 0.2351 - fall_out: 0.0125 - mcc: 0.7977 - val_loss: 0.4853 - val_accuracy: 0.8397 - val_recall: 0.7971 - val_precision: 0.8839 - val_AUROC: 0.9847 - val_AUPRC: 0.9162 - val_f1_score: 0.8383 - val_balanced_accuracy: 0.8927 - val_specificity: 0.9884 - val_miss_rate: 0.2029 - val_fall_out: 0.0116 - val_mcc: 0.8226
Epoch 25/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5288 - accuracy: 0.8254 - recall: 0.7766 - precision: 0.8756 - AUROC: 0.9822 - AUPRC: 0.9043 - f1_score: 0.8231 - balanced_accuracy: 0.8821 - specificity: 0.9877 - miss_rate: 0.2234 - fall_out: 0.0123 - mcc: 0.8065 - val_loss: 0.4603 - val_accuracy: 0.8467 - val_recall: 0.8101 - val_precision: 0.8885 - val_AUROC: 0.9866 - val_AUPRC: 0.9242 - val_f1_score: 0.8475 - val_balanced_accuracy: 0.8994 - val_specificity: 0.9887 - val_miss_rate: 0.1899 - val_fall_out: 0.0113 - val_mcc: 0.8325
Epoch 26/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5224 - accuracy: 0.8353 - recall: 0.7842 - precision: 0.8800 - AUROC: 0.9817 - AUPRC: 0.9070 - f1_score: 0.8293 - balanced_accuracy: 0.8862 - specificity: 0.9881 - miss_rate: 0.2158 - fall_out: 0.0119 - mcc: 0.8132 - val_loss: 0.4545 - val_accuracy: 0.8527 - val_recall: 0.8196 - val_precision: 0.8959 - val_AUROC: 0.9866 - val_AUPRC: 0.9253 - val_f1_score: 0.8561 - val_balanced_accuracy: 0.9045 - val_specificity: 0.9894 - val_miss_rate: 0.1804 - val_fall_out: 0.0106 - val_mcc: 0.8419
Epoch 27/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4985 - accuracy: 0.8354 - recall: 0.7930 - precision: 0.8810 - AUROC: 0.9835 - AUPRC: 0.9140 - f1_score: 0.8347 - balanced_accuracy: 0.8905 - specificity: 0.9881 - miss_rate: 0.2070 - fall_out: 0.0119 - mcc: 0.8188 - val_loss: 0.4344 - val_accuracy: 0.8617 - val_recall: 0.8252 - val_precision: 0.8922 - val_AUROC: 0.9874 - val_AUPRC: 0.9304 - val_f1_score: 0.8574 - val_balanced_accuracy: 0.9070 - val_specificity: 0.9889 - val_miss_rate: 0.1748 - val_fall_out: 0.0111 - val_mcc: 0.8430
Epoch 28/100
63/63 [==============================] - 1s 9ms/step - loss: 0.4959 - accuracy: 0.8406 - recall: 0.7971 - precision: 0.8876 - AUROC: 0.9837 - AUPRC: 0.9121 - f1_score: 0.8399 - balanced_accuracy: 0.8929 - specificity: 0.9888 - miss_rate: 0.2029 - fall_out: 0.0112 - mcc: 0.8246 - val_loss: 0.4254 - val_accuracy: 0.8632 - val_recall: 0.8287 - val_precision: 0.8970 - val_AUROC: 0.9882 - val_AUPRC: 0.9331 - val_f1_score: 0.8615 - val_balanced_accuracy: 0.9090 - val_specificity: 0.9894 - val_miss_rate: 0.1713 - val_fall_out: 0.0106 - val_mcc: 0.8476
Epoch 29/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4655 - accuracy: 0.8496 - recall: 0.8074 - precision: 0.8914 - AUROC: 0.9857 - AUPRC: 0.9221 - f1_score: 0.8473 - balanced_accuracy: 0.8982 - specificity: 0.9891 - miss_rate: 0.1926 - fall_out: 0.0109 - mcc: 0.8325 - val_loss: 0.4252 - val_accuracy: 0.8607 - val_recall: 0.8377 - val_precision: 0.8956 - val_AUROC: 0.9878 - val_AUPRC: 0.9330 - val_f1_score: 0.8656 - val_balanced_accuracy: 0.9134 - val_specificity: 0.9891 - val_miss_rate: 0.1623 - val_fall_out: 0.0109 - val_mcc: 0.8519
Epoch 30/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4740 - accuracy: 0.8451 - recall: 0.8019 - precision: 0.8887 - AUROC: 0.9849 - AUPRC: 0.9195 - f1_score: 0.8430 - balanced_accuracy: 0.8953 - specificity: 0.9888 - miss_rate: 0.1981 - fall_out: 0.0112 - mcc: 0.8279 - val_loss: 0.4209 - val_accuracy: 0.8637 - val_recall: 0.8347 - val_precision: 0.8952 - val_AUROC: 0.9876 - val_AUPRC: 0.9345 - val_f1_score: 0.8639 - val_balanced_accuracy: 0.9119 - val_specificity: 0.9891 - val_miss_rate: 0.1653 - val_fall_out: 0.0109 - val_mcc: 0.8500
Epoch 31/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4432 - accuracy: 0.8572 - recall: 0.8181 - precision: 0.8936 - AUROC: 0.9867 - AUPRC: 0.9287 - f1_score: 0.8542 - balanced_accuracy: 0.9037 - specificity: 0.9892 - miss_rate: 0.1819 - fall_out: 0.0108 - mcc: 0.8398 - val_loss: 0.4124 - val_accuracy: 0.8672 - val_recall: 0.8412 - val_precision: 0.8940 - val_AUROC: 0.9883 - val_AUPRC: 0.9375 - val_f1_score: 0.8668 - val_balanced_accuracy: 0.9151 - val_specificity: 0.9889 - val_miss_rate: 0.1588 - val_fall_out: 0.0111 - val_mcc: 0.8530
Epoch 32/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4370 - accuracy: 0.8586 - recall: 0.8246 - precision: 0.8970 - AUROC: 0.9873 - AUPRC: 0.9299 - f1_score: 0.8593 - balanced_accuracy: 0.9071 - specificity: 0.9895 - miss_rate: 0.1754 - fall_out: 0.0105 - mcc: 0.8453 - val_loss: 0.4159 - val_accuracy: 0.8662 - val_recall: 0.8497 - val_precision: 0.8964 - val_AUROC: 0.9878 - val_AUPRC: 0.9350 - val_f1_score: 0.8724 - val_balanced_accuracy: 0.9194 - val_specificity: 0.9891 - val_miss_rate: 0.1503 - val_fall_out: 0.0109 - val_mcc: 0.8591
Epoch 33/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4379 - accuracy: 0.8612 - recall: 0.8270 - precision: 0.8975 - AUROC: 0.9864 - AUPRC: 0.9292 - f1_score: 0.8608 - balanced_accuracy: 0.9083 - specificity: 0.9895 - miss_rate: 0.1730 - fall_out: 0.0105 - mcc: 0.8469 - val_loss: 0.3993 - val_accuracy: 0.8702 - val_recall: 0.8462 - val_precision: 0.8998 - val_AUROC: 0.9890 - val_AUPRC: 0.9410 - val_f1_score: 0.8722 - val_balanced_accuracy: 0.9179 - val_specificity: 0.9895 - val_miss_rate: 0.1538 - val_fall_out: 0.0105 - val_mcc: 0.8590
Epoch 34/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4218 - accuracy: 0.8679 - recall: 0.8315 - precision: 0.9025 - AUROC: 0.9877 - AUPRC: 0.9330 - f1_score: 0.8656 - balanced_accuracy: 0.9108 - specificity: 0.9900 - miss_rate: 0.1685 - fall_out: 0.0100 - mcc: 0.8522 - val_loss: 0.3974 - val_accuracy: 0.8712 - val_recall: 0.8492 - val_precision: 0.9011 - val_AUROC: 0.9892 - val_AUPRC: 0.9408 - val_f1_score: 0.8744 - val_balanced_accuracy: 0.9194 - val_specificity: 0.9896 - val_miss_rate: 0.1508 - val_fall_out: 0.0104 - val_mcc: 0.8614
Epoch 35/100
63/63 [==============================] - 1s 9ms/step - loss: 0.4185 - accuracy: 0.8635 - recall: 0.8349 - precision: 0.8992 - AUROC: 0.9877 - AUPRC: 0.9323 - f1_score: 0.8659 - balanced_accuracy: 0.9123 - specificity: 0.9896 - miss_rate: 0.1651 - fall_out: 0.0104 - mcc: 0.8523 - val_loss: 0.3862 - val_accuracy: 0.8773 - val_recall: 0.8472 - val_precision: 0.9072 - val_AUROC: 0.9896 - val_AUPRC: 0.9443 - val_f1_score: 0.8762 - val_balanced_accuracy: 0.9188 - val_specificity: 0.9904 - val_miss_rate: 0.1528 - val_fall_out: 0.0096 - val_mcc: 0.8635
Epoch 36/100
63/63 [==============================] - 1s 9ms/step - loss: 0.4015 - accuracy: 0.8714 - recall: 0.8378 - precision: 0.9033 - AUROC: 0.9888 - AUPRC: 0.9386 - f1_score: 0.8693 - balanced_accuracy: 0.9139 - specificity: 0.9900 - miss_rate: 0.1622 - fall_out: 0.0100 - mcc: 0.8561 - val_loss: 0.3803 - val_accuracy: 0.8783 - val_recall: 0.8542 - val_precision: 0.9021 - val_AUROC: 0.9904 - val_AUPRC: 0.9460 - val_f1_score: 0.8775 - val_balanced_accuracy: 0.9220 - val_specificity: 0.9897 - val_miss_rate: 0.1458 - val_fall_out: 0.0103 - val_mcc: 0.8647
Epoch 37/100
63/63 [==============================] - 1s 9ms/step - loss: 0.3922 - accuracy: 0.8747 - recall: 0.8431 - precision: 0.9041 - AUROC: 0.9888 - AUPRC: 0.9407 - f1_score: 0.8725 - balanced_accuracy: 0.9166 - specificity: 0.9901 - miss_rate: 0.1569 - fall_out: 0.0099 - mcc: 0.8595 - val_loss: 0.3747 - val_accuracy: 0.8803 - val_recall: 0.8582 - val_precision: 0.9016 - val_AUROC: 0.9903 - val_AUPRC: 0.9465 - val_f1_score: 0.8794 - val_balanced_accuracy: 0.9239 - val_specificity: 0.9896 - val_miss_rate: 0.1418 - val_fall_out: 0.0104 - val_mcc: 0.8666
Epoch 38/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4008 - accuracy: 0.8705 - recall: 0.8421 - precision: 0.9031 - AUROC: 0.9885 - AUPRC: 0.9377 - f1_score: 0.8715 - balanced_accuracy: 0.9160 - specificity: 0.9900 - miss_rate: 0.1579 - fall_out: 0.0100 - mcc: 0.8585 - val_loss: 0.3758 - val_accuracy: 0.8828 - val_recall: 0.8617 - val_precision: 0.9053 - val_AUROC: 0.9904 - val_AUPRC: 0.9460 - val_f1_score: 0.8830 - val_balanced_accuracy: 0.9259 - val_specificity: 0.9900 - val_miss_rate: 0.1383 - val_fall_out: 0.0100 - val_mcc: 0.8706
Epoch 39/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3854 - accuracy: 0.8727 - recall: 0.8462 - precision: 0.9055 - AUROC: 0.9892 - AUPRC: 0.9429 - f1_score: 0.8748 - balanced_accuracy: 0.9182 - specificity: 0.9902 - miss_rate: 0.1538 - fall_out: 0.0098 - mcc: 0.8621 - val_loss: 0.3690 - val_accuracy: 0.8843 - val_recall: 0.8642 - val_precision: 0.9065 - val_AUROC: 0.9904 - val_AUPRC: 0.9472 - val_f1_score: 0.8848 - val_balanced_accuracy: 0.9272 - val_specificity: 0.9901 - val_miss_rate: 0.1358 - val_fall_out: 0.0099 - val_mcc: 0.8727
Epoch 40/100
63/63 [==============================] - 1s 9ms/step - loss: 0.3757 - accuracy: 0.8770 - recall: 0.8502 - precision: 0.9088 - AUROC: 0.9901 - AUPRC: 0.9452 - f1_score: 0.8785 - balanced_accuracy: 0.9204 - specificity: 0.9905 - miss_rate: 0.1498 - fall_out: 0.0095 - mcc: 0.8661 - val_loss: 0.3631 - val_accuracy: 0.8813 - val_recall: 0.8567 - val_precision: 0.9033 - val_AUROC: 0.9904 - val_AUPRC: 0.9488 - val_f1_score: 0.8794 - val_balanced_accuracy: 0.9233 - val_specificity: 0.9898 - val_miss_rate: 0.1433 - val_fall_out: 0.0102 - val_mcc: 0.8668
Epoch 41/100
63/63 [==============================] - 1s 9ms/step - loss: 0.3731 - accuracy: 0.8776 - recall: 0.8523 - precision: 0.9079 - AUROC: 0.9902 - AUPRC: 0.9458 - f1_score: 0.8793 - balanced_accuracy: 0.9214 - specificity: 0.9904 - miss_rate: 0.1477 - fall_out: 0.0096 - mcc: 0.8668 - val_loss: 0.3643 - val_accuracy: 0.8873 - val_recall: 0.8672 - val_precision: 0.9044 - val_AUROC: 0.9908 - val_AUPRC: 0.9493 - val_f1_score: 0.8854 - val_balanced_accuracy: 0.9285 - val_specificity: 0.9898 - val_miss_rate: 0.1328 - val_fall_out: 0.0102 - val_mcc: 0.8732
Epoch 42/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3510 - accuracy: 0.8844 - recall: 0.8577 - precision: 0.9102 - AUROC: 0.9915 - AUPRC: 0.9517 - f1_score: 0.8832 - balanced_accuracy: 0.9242 - specificity: 0.9906 - miss_rate: 0.1423 - fall_out: 0.0094 - mcc: 0.8711 - val_loss: 0.3607 - val_accuracy: 0.8828 - val_recall: 0.8652 - val_precision: 0.9004 - val_AUROC: 0.9906 - val_AUPRC: 0.9510 - val_f1_score: 0.8825 - val_balanced_accuracy: 0.9273 - val_specificity: 0.9894 - val_miss_rate: 0.1348 - val_fall_out: 0.0106 - val_mcc: 0.8699
Epoch 43/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3487 - accuracy: 0.8871 - recall: 0.8625 - precision: 0.9133 - AUROC: 0.9909 - AUPRC: 0.9510 - f1_score: 0.8871 - balanced_accuracy: 0.9267 - specificity: 0.9909 - miss_rate: 0.1375 - fall_out: 0.0091 - mcc: 0.8754 - val_loss: 0.3601 - val_accuracy: 0.8808 - val_recall: 0.8617 - val_precision: 0.9034 - val_AUROC: 0.9913 - val_AUPRC: 0.9508 - val_f1_score: 0.8821 - val_balanced_accuracy: 0.9257 - val_specificity: 0.9898 - val_miss_rate: 0.1383 - val_fall_out: 0.0102 - val_mcc: 0.8696
Epoch 44/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3397 - accuracy: 0.8908 - recall: 0.8670 - precision: 0.9172 - AUROC: 0.9914 - AUPRC: 0.9537 - f1_score: 0.8914 - balanced_accuracy: 0.9291 - specificity: 0.9913 - miss_rate: 0.1330 - fall_out: 0.0087 - mcc: 0.8801 - val_loss: 0.3528 - val_accuracy: 0.8868 - val_recall: 0.8692 - val_precision: 0.9089 - val_AUROC: 0.9906 - val_AUPRC: 0.9509 - val_f1_score: 0.8886 - val_balanced_accuracy: 0.9298 - val_specificity: 0.9903 - val_miss_rate: 0.1308 - val_fall_out: 0.0097 - val_mcc: 0.8768
Epoch 45/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3338 - accuracy: 0.8919 - recall: 0.8710 - precision: 0.9183 - AUROC: 0.9920 - AUPRC: 0.9546 - f1_score: 0.8940 - balanced_accuracy: 0.9312 - specificity: 0.9914 - miss_rate: 0.1290 - fall_out: 0.0086 - mcc: 0.8829 - val_loss: 0.3389 - val_accuracy: 0.8913 - val_recall: 0.8763 - val_precision: 0.9100 - val_AUROC: 0.9918 - val_AUPRC: 0.9555 - val_f1_score: 0.8928 - val_balanced_accuracy: 0.9333 - val_specificity: 0.9904 - val_miss_rate: 0.1237 - val_fall_out: 0.0096 - val_mcc: 0.8813
Epoch 46/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3252 - accuracy: 0.8937 - recall: 0.8721 - precision: 0.9165 - AUROC: 0.9923 - AUPRC: 0.9564 - f1_score: 0.8938 - balanced_accuracy: 0.9316 - specificity: 0.9912 - miss_rate: 0.1279 - fall_out: 0.0088 - mcc: 0.8826 - val_loss: 0.3483 - val_accuracy: 0.8863 - val_recall: 0.8727 - val_precision: 0.9040 - val_AUROC: 0.9910 - val_AUPRC: 0.9542 - val_f1_score: 0.8881 - val_balanced_accuracy: 0.9312 - val_specificity: 0.9897 - val_miss_rate: 0.1273 - val_fall_out: 0.0103 - val_mcc: 0.8761
Epoch 47/100
63/63 [==============================] - 1s 9ms/step - loss: 0.3332 - accuracy: 0.8972 - recall: 0.8737 - precision: 0.9198 - AUROC: 0.9914 - AUPRC: 0.9555 - f1_score: 0.8962 - balanced_accuracy: 0.9326 - specificity: 0.9915 - miss_rate: 0.1263 - fall_out: 0.0085 - mcc: 0.8854 - val_loss: 0.3416 - val_accuracy: 0.8888 - val_recall: 0.8758 - val_precision: 0.9118 - val_AUROC: 0.9909 - val_AUPRC: 0.9544 - val_f1_score: 0.8934 - val_balanced_accuracy: 0.9332 - val_specificity: 0.9906 - val_miss_rate: 0.1242 - val_fall_out: 0.0094 - val_mcc: 0.8821
250/250 [==============================] - 1s 5ms/step - loss: 0.0987 - accuracy: 0.9733 - recall: 0.9647 - precision: 0.9815 - AUROC: 0.9996 - AUPRC: 0.9966 - f1_score: 0.9730 - balanced_accuracy: 0.9813 - specificity: 0.9980 - miss_rate: 0.0353 - fall_out: 0.0020 - mcc: 0.9701
63/63 [==============================] - 0s 4ms/step - loss: 0.3416 - accuracy: 0.8888 - recall: 0.8758 - precision: 0.9118 - AUROC: 0.9909 - AUPRC: 0.9544 - f1_score: 0.8934 - balanced_accuracy: 0.9332 - specificity: 0.9906 - miss_rate: 0.1242 - fall_out: 0.0094 - mcc: 0.8821
2it [01:11, 35.71s/it]
-- HOLDOUT 3 -- WINDOW window_3s
-- 6 Uncorrelated features: [Pearson+Spearman]
['mfcc16_mean', 'tempo', 'mfcc10_var', 'mfcc19_mean', 'mfcc11_var', 'mfcc17_mean']
-- Actually uncorrelated features: [confirmed via MIC]
[]
-- 1 Correlated features: [Pearson]
['spectral_centroid_mean']
Model: "MLP"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_230 (Dense) (None, 256) 14592
dropout_178 (Dropout) (None, 256) 0
dense_231 (Dense) (None, 256) 65792
dropout_179 (Dropout) (None, 256) 0
dense_232 (Dense) (None, 128) 32896
dropout_180 (Dropout) (None, 128) 0
dense_233 (Dense) (None, 128) 16512
dropout_181 (Dropout) (None, 128) 0
dense_234 (Dense) (None, 10) 1290
=================================================================
Total params: 131,082
Trainable params: 131,082
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 2s 18ms/step - loss: 2.2366 - accuracy: 0.2035 - recall: 0.0222 - precision: 0.4574 - AUROC: 0.6433 - AUPRC: 0.1854 - f1_score: 0.0423 - balanced_accuracy: 0.5096 - specificity: 0.9971 - miss_rate: 0.9778 - fall_out: 0.0029 - mcc: 0.0831 - val_loss: 1.7186 - val_accuracy: 0.4083 - val_recall: 0.1393 - val_precision: 0.8176 - val_AUROC: 0.8446 - val_AUPRC: 0.4439 - val_f1_score: 0.2380 - val_balanced_accuracy: 0.5679 - val_specificity: 0.9965 - val_miss_rate: 0.8607 - val_fall_out: 0.0035 - val_mcc: 0.3149
Epoch 2/100
63/63 [==============================] - 1s 9ms/step - loss: 1.7801 - accuracy: 0.3694 - recall: 0.1556 - precision: 0.6432 - AUROC: 0.8111 - AUPRC: 0.3848 - f1_score: 0.2505 - balanced_accuracy: 0.5730 - specificity: 0.9904 - miss_rate: 0.8444 - fall_out: 0.0096 - mcc: 0.2851 - val_loss: 1.4420 - val_accuracy: 0.4950 - val_recall: 0.2505 - val_precision: 0.7267 - val_AUROC: 0.8935 - val_AUPRC: 0.5480 - val_f1_score: 0.3726 - val_balanced_accuracy: 0.6200 - val_specificity: 0.9895 - val_miss_rate: 0.7495 - val_fall_out: 0.0105 - val_mcc: 0.3947
Epoch 3/100
63/63 [==============================] - 1s 9ms/step - loss: 1.5719 - accuracy: 0.4419 - recall: 0.2233 - precision: 0.6761 - AUROC: 0.8601 - AUPRC: 0.4745 - f1_score: 0.3357 - balanced_accuracy: 0.6057 - specificity: 0.9881 - miss_rate: 0.7767 - fall_out: 0.0119 - mcc: 0.3549 - val_loss: 1.2531 - val_accuracy: 0.5566 - val_recall: 0.3026 - val_precision: 0.7617 - val_AUROC: 0.9197 - val_AUPRC: 0.6226 - val_f1_score: 0.4331 - val_balanced_accuracy: 0.6460 - val_specificity: 0.9895 - val_miss_rate: 0.6974 - val_fall_out: 0.0105 - val_mcc: 0.4486
Epoch 4/100
63/63 [==============================] - 1s 10ms/step - loss: 1.4242 - accuracy: 0.5030 - recall: 0.2757 - precision: 0.6992 - AUROC: 0.8869 - AUPRC: 0.5382 - f1_score: 0.3954 - balanced_accuracy: 0.6312 - specificity: 0.9868 - miss_rate: 0.7243 - fall_out: 0.0132 - mcc: 0.4046 - val_loss: 1.1219 - val_accuracy: 0.6117 - val_recall: 0.3572 - val_precision: 0.8075 - val_AUROC: 0.9356 - val_AUPRC: 0.6819 - val_f1_score: 0.4953 - val_balanced_accuracy: 0.6739 - val_specificity: 0.9905 - val_miss_rate: 0.6428 - val_fall_out: 0.0095 - val_mcc: 0.5074
Epoch 5/100
63/63 [==============================] - 1s 10ms/step - loss: 1.2941 - accuracy: 0.5510 - recall: 0.3439 - precision: 0.7292 - AUROC: 0.9067 - AUPRC: 0.5955 - f1_score: 0.4674 - balanced_accuracy: 0.6649 - specificity: 0.9858 - miss_rate: 0.6561 - fall_out: 0.0142 - mcc: 0.4666 - val_loss: 1.0378 - val_accuracy: 0.6493 - val_recall: 0.4018 - val_precision: 0.8192 - val_AUROC: 0.9451 - val_AUPRC: 0.7174 - val_f1_score: 0.5392 - val_balanced_accuracy: 0.6960 - val_specificity: 0.9901 - val_miss_rate: 0.5982 - val_fall_out: 0.0099 - val_mcc: 0.5445
Epoch 6/100
63/63 [==============================] - 1s 9ms/step - loss: 1.1952 - accuracy: 0.5888 - recall: 0.4006 - precision: 0.7518 - AUROC: 0.9207 - AUPRC: 0.6444 - f1_score: 0.5226 - balanced_accuracy: 0.6929 - specificity: 0.9853 - miss_rate: 0.5994 - fall_out: 0.0147 - mcc: 0.5154 - val_loss: 0.9391 - val_accuracy: 0.6764 - val_recall: 0.4900 - val_precision: 0.8309 - val_AUROC: 0.9541 - val_AUPRC: 0.7630 - val_f1_score: 0.6165 - val_balanced_accuracy: 0.7395 - val_specificity: 0.9889 - val_miss_rate: 0.5100 - val_fall_out: 0.0111 - val_mcc: 0.6099
Epoch 7/100
63/63 [==============================] - 1s 9ms/step - loss: 1.1225 - accuracy: 0.6265 - recall: 0.4460 - precision: 0.7593 - AUROC: 0.9294 - AUPRC: 0.6783 - f1_score: 0.5619 - balanced_accuracy: 0.7152 - specificity: 0.9843 - miss_rate: 0.5540 - fall_out: 0.0157 - mcc: 0.5490 - val_loss: 0.8683 - val_accuracy: 0.7189 - val_recall: 0.5306 - val_precision: 0.8716 - val_AUROC: 0.9619 - val_AUPRC: 0.8053 - val_f1_score: 0.6596 - val_balanced_accuracy: 0.7609 - val_specificity: 0.9913 - val_miss_rate: 0.4694 - val_fall_out: 0.0087 - val_mcc: 0.6548
Epoch 8/100
63/63 [==============================] - 1s 10ms/step - loss: 1.0472 - accuracy: 0.6497 - recall: 0.4855 - precision: 0.7797 - AUROC: 0.9381 - AUPRC: 0.7116 - f1_score: 0.5984 - balanced_accuracy: 0.7351 - specificity: 0.9848 - miss_rate: 0.5145 - fall_out: 0.0152 - mcc: 0.5838 - val_loss: 0.8348 - val_accuracy: 0.7214 - val_recall: 0.5681 - val_precision: 0.8388 - val_AUROC: 0.9619 - val_AUPRC: 0.8055 - val_f1_score: 0.6774 - val_balanced_accuracy: 0.7780 - val_specificity: 0.9879 - val_miss_rate: 0.4319 - val_fall_out: 0.0121 - val_mcc: 0.6638
Epoch 9/100
63/63 [==============================] - 1s 9ms/step - loss: 0.9955 - accuracy: 0.6732 - recall: 0.5217 - precision: 0.7838 - AUROC: 0.9433 - AUPRC: 0.7321 - f1_score: 0.6264 - balanced_accuracy: 0.7528 - specificity: 0.9840 - miss_rate: 0.4783 - fall_out: 0.0160 - mcc: 0.6086 - val_loss: 0.7610 - val_accuracy: 0.7470 - val_recall: 0.6263 - val_precision: 0.8406 - val_AUROC: 0.9684 - val_AUPRC: 0.8331 - val_f1_score: 0.7178 - val_balanced_accuracy: 0.8065 - val_specificity: 0.9868 - val_miss_rate: 0.3737 - val_fall_out: 0.0132 - val_mcc: 0.7004
Epoch 10/100
63/63 [==============================] - 1s 9ms/step - loss: 0.9423 - accuracy: 0.6857 - recall: 0.5531 - precision: 0.7921 - AUROC: 0.9490 - AUPRC: 0.7565 - f1_score: 0.6514 - balanced_accuracy: 0.7685 - specificity: 0.9839 - miss_rate: 0.4469 - fall_out: 0.0161 - mcc: 0.6321 - val_loss: 0.7390 - val_accuracy: 0.7490 - val_recall: 0.6463 - val_precision: 0.8577 - val_AUROC: 0.9698 - val_AUPRC: 0.8418 - val_f1_score: 0.7371 - val_balanced_accuracy: 0.8172 - val_specificity: 0.9881 - val_miss_rate: 0.3537 - val_fall_out: 0.0119 - val_mcc: 0.7210
Epoch 11/100
63/63 [==============================] - 1s 9ms/step - loss: 0.8890 - accuracy: 0.7100 - recall: 0.5814 - precision: 0.8067 - AUROC: 0.9539 - AUPRC: 0.7805 - f1_score: 0.6758 - balanced_accuracy: 0.7830 - specificity: 0.9845 - miss_rate: 0.4186 - fall_out: 0.0155 - mcc: 0.6565 - val_loss: 0.7147 - val_accuracy: 0.7580 - val_recall: 0.6548 - val_precision: 0.8416 - val_AUROC: 0.9711 - val_AUPRC: 0.8471 - val_f1_score: 0.7365 - val_balanced_accuracy: 0.8206 - val_specificity: 0.9863 - val_miss_rate: 0.3452 - val_fall_out: 0.0137 - val_mcc: 0.7180
Epoch 12/100
63/63 [==============================] - 1s 9ms/step - loss: 0.8540 - accuracy: 0.7213 - recall: 0.6042 - precision: 0.8182 - AUROC: 0.9573 - AUPRC: 0.7919 - f1_score: 0.6951 - balanced_accuracy: 0.7946 - specificity: 0.9851 - miss_rate: 0.3958 - fall_out: 0.0149 - mcc: 0.6760 - val_loss: 0.6715 - val_accuracy: 0.7761 - val_recall: 0.6889 - val_precision: 0.8637 - val_AUROC: 0.9740 - val_AUPRC: 0.8666 - val_f1_score: 0.7664 - val_balanced_accuracy: 0.8384 - val_specificity: 0.9879 - val_miss_rate: 0.3111 - val_fall_out: 0.0121 - val_mcc: 0.7494
Epoch 13/100
63/63 [==============================] - 1s 9ms/step - loss: 0.8076 - accuracy: 0.7323 - recall: 0.6296 - precision: 0.8293 - AUROC: 0.9616 - AUPRC: 0.8106 - f1_score: 0.7158 - balanced_accuracy: 0.8076 - specificity: 0.9856 - miss_rate: 0.3704 - fall_out: 0.0144 - mcc: 0.6968 - val_loss: 0.6404 - val_accuracy: 0.7811 - val_recall: 0.7049 - val_precision: 0.8643 - val_AUROC: 0.9762 - val_AUPRC: 0.8744 - val_f1_score: 0.7765 - val_balanced_accuracy: 0.8463 - val_specificity: 0.9877 - val_miss_rate: 0.2951 - val_fall_out: 0.0123 - val_mcc: 0.7592
Epoch 14/100
63/63 [==============================] - 1s 9ms/step - loss: 0.7726 - accuracy: 0.7439 - recall: 0.6494 - precision: 0.8242 - AUROC: 0.9650 - AUPRC: 0.8227 - f1_score: 0.7264 - balanced_accuracy: 0.8170 - specificity: 0.9846 - miss_rate: 0.3506 - fall_out: 0.0154 - mcc: 0.7060 - val_loss: 0.6317 - val_accuracy: 0.7921 - val_recall: 0.7159 - val_precision: 0.8724 - val_AUROC: 0.9764 - val_AUPRC: 0.8752 - val_f1_score: 0.7865 - val_balanced_accuracy: 0.8521 - val_specificity: 0.9884 - val_miss_rate: 0.2841 - val_fall_out: 0.0116 - val_mcc: 0.7698
Epoch 15/100
63/63 [==============================] - 1s 9ms/step - loss: 0.7606 - accuracy: 0.7540 - recall: 0.6584 - precision: 0.8354 - AUROC: 0.9650 - AUPRC: 0.8270 - f1_score: 0.7364 - balanced_accuracy: 0.8220 - specificity: 0.9856 - miss_rate: 0.3416 - fall_out: 0.0144 - mcc: 0.7170 - val_loss: 0.6017 - val_accuracy: 0.7996 - val_recall: 0.7194 - val_precision: 0.8719 - val_AUROC: 0.9787 - val_AUPRC: 0.8854 - val_f1_score: 0.7884 - val_balanced_accuracy: 0.8538 - val_specificity: 0.9883 - val_miss_rate: 0.2806 - val_fall_out: 0.0117 - val_mcc: 0.7716
Epoch 16/100
63/63 [==============================] - 1s 9ms/step - loss: 0.7148 - accuracy: 0.7739 - recall: 0.6841 - precision: 0.8450 - AUROC: 0.9691 - AUPRC: 0.8444 - f1_score: 0.7561 - balanced_accuracy: 0.8351 - specificity: 0.9861 - miss_rate: 0.3159 - fall_out: 0.0139 - mcc: 0.7371 - val_loss: 0.5694 - val_accuracy: 0.8036 - val_recall: 0.7370 - val_precision: 0.8766 - val_AUROC: 0.9806 - val_AUPRC: 0.8960 - val_f1_score: 0.8008 - val_balanced_accuracy: 0.8627 - val_specificity: 0.9885 - val_miss_rate: 0.2630 - val_fall_out: 0.0115 - val_mcc: 0.7843
Epoch 17/100
63/63 [==============================] - 1s 9ms/step - loss: 0.7223 - accuracy: 0.7687 - recall: 0.6859 - precision: 0.8458 - AUROC: 0.9684 - AUPRC: 0.8430 - f1_score: 0.7575 - balanced_accuracy: 0.8360 - specificity: 0.9861 - miss_rate: 0.3141 - fall_out: 0.0139 - mcc: 0.7385 - val_loss: 0.5576 - val_accuracy: 0.8046 - val_recall: 0.7495 - val_precision: 0.8754 - val_AUROC: 0.9813 - val_AUPRC: 0.8998 - val_f1_score: 0.8076 - val_balanced_accuracy: 0.8688 - val_specificity: 0.9881 - val_miss_rate: 0.2505 - val_fall_out: 0.0119 - val_mcc: 0.7909
Epoch 18/100
63/63 [==============================] - 1s 9ms/step - loss: 0.6715 - accuracy: 0.7834 - recall: 0.7055 - precision: 0.8513 - AUROC: 0.9726 - AUPRC: 0.8588 - f1_score: 0.7716 - balanced_accuracy: 0.8459 - specificity: 0.9863 - miss_rate: 0.2945 - fall_out: 0.0137 - mcc: 0.7528 - val_loss: 0.5318 - val_accuracy: 0.8241 - val_recall: 0.7685 - val_precision: 0.8796 - val_AUROC: 0.9826 - val_AUPRC: 0.9059 - val_f1_score: 0.8203 - val_balanced_accuracy: 0.8784 - val_specificity: 0.9883 - val_miss_rate: 0.2315 - val_fall_out: 0.0117 - val_mcc: 0.8041
Epoch 19/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6480 - accuracy: 0.7902 - recall: 0.7199 - precision: 0.8503 - AUROC: 0.9745 - AUPRC: 0.8668 - f1_score: 0.7797 - balanced_accuracy: 0.8529 - specificity: 0.9859 - miss_rate: 0.2801 - fall_out: 0.0141 - mcc: 0.7607 - val_loss: 0.5256 - val_accuracy: 0.8191 - val_recall: 0.7685 - val_precision: 0.8791 - val_AUROC: 0.9827 - val_AUPRC: 0.9070 - val_f1_score: 0.8201 - val_balanced_accuracy: 0.8784 - val_specificity: 0.9883 - val_miss_rate: 0.2315 - val_fall_out: 0.0117 - val_mcc: 0.8038
Epoch 20/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6316 - accuracy: 0.7980 - recall: 0.7312 - precision: 0.8603 - AUROC: 0.9750 - AUPRC: 0.8735 - f1_score: 0.7905 - balanced_accuracy: 0.8590 - specificity: 0.9868 - miss_rate: 0.2688 - fall_out: 0.0132 - mcc: 0.7724 - val_loss: 0.5063 - val_accuracy: 0.8337 - val_recall: 0.7791 - val_precision: 0.8815 - val_AUROC: 0.9848 - val_AUPRC: 0.9143 - val_f1_score: 0.8271 - val_balanced_accuracy: 0.8837 - val_specificity: 0.9884 - val_miss_rate: 0.2209 - val_fall_out: 0.0116 - val_mcc: 0.8111
Epoch 21/100
63/63 [==============================] - 1s 9ms/step - loss: 0.6271 - accuracy: 0.7940 - recall: 0.7291 - precision: 0.8546 - AUROC: 0.9764 - AUPRC: 0.8743 - f1_score: 0.7869 - balanced_accuracy: 0.8577 - specificity: 0.9862 - miss_rate: 0.2709 - fall_out: 0.0138 - mcc: 0.7682 - val_loss: 0.4943 - val_accuracy: 0.8397 - val_recall: 0.7856 - val_precision: 0.8824 - val_AUROC: 0.9849 - val_AUPRC: 0.9158 - val_f1_score: 0.8312 - val_balanced_accuracy: 0.8870 - val_specificity: 0.9884 - val_miss_rate: 0.2144 - val_fall_out: 0.0116 - val_mcc: 0.8153
Epoch 22/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5928 - accuracy: 0.8104 - recall: 0.7500 - precision: 0.8662 - AUROC: 0.9779 - AUPRC: 0.8850 - f1_score: 0.8039 - balanced_accuracy: 0.8686 - specificity: 0.9871 - miss_rate: 0.2500 - fall_out: 0.0129 - mcc: 0.7863 - val_loss: 0.4898 - val_accuracy: 0.8352 - val_recall: 0.7966 - val_precision: 0.8863 - val_AUROC: 0.9844 - val_AUPRC: 0.9164 - val_f1_score: 0.8391 - val_balanced_accuracy: 0.8926 - val_specificity: 0.9886 - val_miss_rate: 0.2034 - val_fall_out: 0.0114 - val_mcc: 0.8236
Epoch 23/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5717 - accuracy: 0.8170 - recall: 0.7590 - precision: 0.8738 - AUROC: 0.9793 - AUPRC: 0.8923 - f1_score: 0.8124 - balanced_accuracy: 0.8734 - specificity: 0.9878 - miss_rate: 0.2410 - fall_out: 0.0122 - mcc: 0.7956 - val_loss: 0.4731 - val_accuracy: 0.8432 - val_recall: 0.8041 - val_precision: 0.8863 - val_AUROC: 0.9854 - val_AUPRC: 0.9221 - val_f1_score: 0.8432 - val_balanced_accuracy: 0.8963 - val_specificity: 0.9885 - val_miss_rate: 0.1959 - val_fall_out: 0.0115 - val_mcc: 0.8279
Epoch 24/100
63/63 [==============================] - 1s 9ms/step - loss: 0.5484 - accuracy: 0.8230 - recall: 0.7684 - precision: 0.8756 - AUROC: 0.9810 - AUPRC: 0.8989 - f1_score: 0.8185 - balanced_accuracy: 0.8781 - specificity: 0.9879 - miss_rate: 0.2316 - fall_out: 0.0121 - mcc: 0.8018 - val_loss: 0.4549 - val_accuracy: 0.8497 - val_recall: 0.8106 - val_precision: 0.8866 - val_AUROC: 0.9872 - val_AUPRC: 0.9272 - val_f1_score: 0.8469 - val_balanced_accuracy: 0.8995 - val_specificity: 0.9885 - val_miss_rate: 0.1894 - val_fall_out: 0.0115 - val_mcc: 0.8317
Epoch 25/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5415 - accuracy: 0.8213 - recall: 0.7734 - precision: 0.8742 - AUROC: 0.9812 - AUPRC: 0.9023 - f1_score: 0.8207 - balanced_accuracy: 0.8805 - specificity: 0.9876 - miss_rate: 0.2266 - fall_out: 0.0124 - mcc: 0.8040 - val_loss: 0.4650 - val_accuracy: 0.8472 - val_recall: 0.8101 - val_precision: 0.8870 - val_AUROC: 0.9854 - val_AUPRC: 0.9243 - val_f1_score: 0.8468 - val_balanced_accuracy: 0.8993 - val_specificity: 0.9885 - val_miss_rate: 0.1899 - val_fall_out: 0.0115 - val_mcc: 0.8317
Epoch 26/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5255 - accuracy: 0.8312 - recall: 0.7797 - precision: 0.8799 - AUROC: 0.9819 - AUPRC: 0.9040 - f1_score: 0.8267 - balanced_accuracy: 0.8839 - specificity: 0.9882 - miss_rate: 0.2203 - fall_out: 0.0118 - mcc: 0.8106 - val_loss: 0.4488 - val_accuracy: 0.8587 - val_recall: 0.8226 - val_precision: 0.8963 - val_AUROC: 0.9867 - val_AUPRC: 0.9275 - val_f1_score: 0.8579 - val_balanced_accuracy: 0.9060 - val_specificity: 0.9894 - val_miss_rate: 0.1774 - val_fall_out: 0.0106 - val_mcc: 0.8438
Epoch 27/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5158 - accuracy: 0.8383 - recall: 0.7890 - precision: 0.8779 - AUROC: 0.9827 - AUPRC: 0.9087 - f1_score: 0.8311 - balanced_accuracy: 0.8884 - specificity: 0.9878 - miss_rate: 0.2110 - fall_out: 0.0122 - mcc: 0.8148 - val_loss: 0.4313 - val_accuracy: 0.8567 - val_recall: 0.8186 - val_precision: 0.8963 - val_AUROC: 0.9883 - val_AUPRC: 0.9343 - val_f1_score: 0.8557 - val_balanced_accuracy: 0.9041 - val_specificity: 0.9895 - val_miss_rate: 0.1814 - val_fall_out: 0.0105 - val_mcc: 0.8415
Epoch 28/100
63/63 [==============================] - 1s 9ms/step - loss: 0.4877 - accuracy: 0.8418 - recall: 0.7942 - precision: 0.8851 - AUROC: 0.9842 - AUPRC: 0.9156 - f1_score: 0.8372 - balanced_accuracy: 0.8914 - specificity: 0.9885 - miss_rate: 0.2058 - fall_out: 0.0115 - mcc: 0.8217 - val_loss: 0.4219 - val_accuracy: 0.8597 - val_recall: 0.8357 - val_precision: 0.8891 - val_AUROC: 0.9882 - val_AUPRC: 0.9361 - val_f1_score: 0.8616 - val_balanced_accuracy: 0.9120 - val_specificity: 0.9884 - val_miss_rate: 0.1643 - val_fall_out: 0.0116 - val_mcc: 0.8472
Epoch 29/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4876 - accuracy: 0.8428 - recall: 0.8036 - precision: 0.8848 - AUROC: 0.9837 - AUPRC: 0.9158 - f1_score: 0.8423 - balanced_accuracy: 0.8960 - specificity: 0.9884 - miss_rate: 0.1964 - fall_out: 0.0116 - mcc: 0.8268 - val_loss: 0.4290 - val_accuracy: 0.8592 - val_recall: 0.8241 - val_precision: 0.8994 - val_AUROC: 0.9884 - val_AUPRC: 0.9342 - val_f1_score: 0.8601 - val_balanced_accuracy: 0.9070 - val_specificity: 0.9898 - val_miss_rate: 0.1759 - val_fall_out: 0.0102 - val_mcc: 0.8463
Epoch 30/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4713 - accuracy: 0.8476 - recall: 0.8049 - precision: 0.8898 - AUROC: 0.9854 - AUPRC: 0.9202 - f1_score: 0.8452 - balanced_accuracy: 0.8969 - specificity: 0.9889 - miss_rate: 0.1951 - fall_out: 0.0111 - mcc: 0.8302 - val_loss: 0.4049 - val_accuracy: 0.8642 - val_recall: 0.8372 - val_precision: 0.8998 - val_AUROC: 0.9894 - val_AUPRC: 0.9400 - val_f1_score: 0.8674 - val_balanced_accuracy: 0.9134 - val_specificity: 0.9896 - val_miss_rate: 0.1628 - val_fall_out: 0.0104 - val_mcc: 0.8539
Epoch 31/100
63/63 [==============================] - 1s 9ms/step - loss: 0.4588 - accuracy: 0.8516 - recall: 0.8128 - precision: 0.8915 - AUROC: 0.9859 - AUPRC: 0.9252 - f1_score: 0.8503 - balanced_accuracy: 0.9009 - specificity: 0.9890 - miss_rate: 0.1872 - fall_out: 0.0110 - mcc: 0.8356 - val_loss: 0.3977 - val_accuracy: 0.8712 - val_recall: 0.8392 - val_precision: 0.9020 - val_AUROC: 0.9893 - val_AUPRC: 0.9412 - val_f1_score: 0.8695 - val_balanced_accuracy: 0.9145 - val_specificity: 0.9899 - val_miss_rate: 0.1608 - val_fall_out: 0.0101 - val_mcc: 0.8562
Epoch 32/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4578 - accuracy: 0.8573 - recall: 0.8164 - precision: 0.8955 - AUROC: 0.9859 - AUPRC: 0.9227 - f1_score: 0.8541 - balanced_accuracy: 0.9029 - specificity: 0.9894 - miss_rate: 0.1836 - fall_out: 0.0106 - mcc: 0.8398 - val_loss: 0.4027 - val_accuracy: 0.8617 - val_recall: 0.8377 - val_precision: 0.8975 - val_AUROC: 0.9886 - val_AUPRC: 0.9405 - val_f1_score: 0.8665 - val_balanced_accuracy: 0.9135 - val_specificity: 0.9894 - val_miss_rate: 0.1623 - val_fall_out: 0.0106 - val_mcc: 0.8529
Epoch 33/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4542 - accuracy: 0.8566 - recall: 0.8158 - precision: 0.8935 - AUROC: 0.9862 - AUPRC: 0.9262 - f1_score: 0.8529 - balanced_accuracy: 0.9025 - specificity: 0.9892 - miss_rate: 0.1842 - fall_out: 0.0108 - mcc: 0.8384 - val_loss: 0.3985 - val_accuracy: 0.8632 - val_recall: 0.8422 - val_precision: 0.8913 - val_AUROC: 0.9894 - val_AUPRC: 0.9426 - val_f1_score: 0.8660 - val_balanced_accuracy: 0.9154 - val_specificity: 0.9886 - val_miss_rate: 0.1578 - val_fall_out: 0.0114 - val_mcc: 0.8521
250/250 [==============================] - 1s 5ms/step - loss: 0.1797 - accuracy: 0.9456 - recall: 0.9290 - precision: 0.9646 - AUROC: 0.9983 - AUPRC: 0.9877 - f1_score: 0.9465 - balanced_accuracy: 0.9626 - specificity: 0.9962 - miss_rate: 0.0710 - fall_out: 0.0038 - mcc: 0.9408
63/63 [==============================] - 0s 5ms/step - loss: 0.3986 - accuracy: 0.8632 - recall: 0.8422 - precision: 0.8913 - AUROC: 0.9894 - AUPRC: 0.9426 - f1_score: 0.8660 - balanced_accuracy: 0.9154 - specificity: 0.9886 - miss_rate: 0.1578 - fall_out: 0.0114 - mcc: 0.8521
3it [01:38, 31.86s/it]
-- HOLDOUT 4 -- WINDOW window_3s
-- 5 Uncorrelated features: [Pearson+Spearman]
['mfcc16_mean', 'tempo', 'mfcc10_var', 'mfcc19_mean', 'mfcc17_mean']
-- Actually uncorrelated features: [confirmed via MIC]
[]
-- 1 Correlated features: [Pearson]
['spectral_centroid_mean']
Model: "MLP"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_235 (Dense) (None, 256) 14592
dropout_182 (Dropout) (None, 256) 0
dense_236 (Dense) (None, 256) 65792
dropout_183 (Dropout) (None, 256) 0
dense_237 (Dense) (None, 128) 32896
dropout_184 (Dropout) (None, 128) 0
dense_238 (Dense) (None, 128) 16512
dropout_185 (Dropout) (None, 128) 0
dense_239 (Dense) (None, 10) 1290
=================================================================
Total params: 131,082
Trainable params: 131,082
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 2s 18ms/step - loss: 2.2143 - accuracy: 0.2050 - recall: 0.0204 - precision: 0.4553 - AUROC: 0.6540 - AUPRC: 0.1896 - f1_score: 0.0391 - balanced_accuracy: 0.5089 - specificity: 0.9973 - miss_rate: 0.9796 - fall_out: 0.0027 - mcc: 0.0795 - val_loss: 1.7125 - val_accuracy: 0.3878 - val_recall: 0.1328 - val_precision: 0.7749 - val_AUROC: 0.8537 - val_AUPRC: 0.4327 - val_f1_score: 0.2267 - val_balanced_accuracy: 0.5642 - val_specificity: 0.9957 - val_miss_rate: 0.8672 - val_fall_out: 0.0043 - val_mcc: 0.2970
Epoch 2/100
63/63 [==============================] - 1s 9ms/step - loss: 1.7410 - accuracy: 0.3675 - recall: 0.1539 - precision: 0.6173 - AUROC: 0.8235 - AUPRC: 0.3857 - f1_score: 0.2464 - balanced_accuracy: 0.5717 - specificity: 0.9894 - miss_rate: 0.8461 - fall_out: 0.0106 - mcc: 0.2757 - val_loss: 1.3684 - val_accuracy: 0.5175 - val_recall: 0.2515 - val_precision: 0.7641 - val_AUROC: 0.9063 - val_AUPRC: 0.5754 - val_f1_score: 0.3784 - val_balanced_accuracy: 0.6214 - val_specificity: 0.9914 - val_miss_rate: 0.7485 - val_fall_out: 0.0086 - val_mcc: 0.4084
Epoch 3/100
63/63 [==============================] - 1s 9ms/step - loss: 1.5197 - accuracy: 0.4579 - recall: 0.2400 - precision: 0.6773 - AUROC: 0.8703 - AUPRC: 0.4846 - f1_score: 0.3544 - balanced_accuracy: 0.6136 - specificity: 0.9873 - miss_rate: 0.7600 - fall_out: 0.0127 - mcc: 0.3688 - val_loss: 1.2276 - val_accuracy: 0.5611 - val_recall: 0.2996 - val_precision: 0.7257 - val_AUROC: 0.9214 - val_AUPRC: 0.6209 - val_f1_score: 0.4241 - val_balanced_accuracy: 0.6435 - val_specificity: 0.9874 - val_miss_rate: 0.7004 - val_fall_out: 0.0126 - val_mcc: 0.4328
Epoch 4/100
63/63 [==============================] - 1s 9ms/step - loss: 1.3787 - accuracy: 0.5130 - recall: 0.2983 - precision: 0.7033 - AUROC: 0.8945 - AUPRC: 0.5523 - f1_score: 0.4190 - balanced_accuracy: 0.6422 - specificity: 0.9860 - miss_rate: 0.7017 - fall_out: 0.0140 - mcc: 0.4233 - val_loss: 1.1022 - val_accuracy: 0.6152 - val_recall: 0.3863 - val_precision: 0.7741 - val_AUROC: 0.9351 - val_AUPRC: 0.6808 - val_f1_score: 0.5154 - val_balanced_accuracy: 0.6869 - val_specificity: 0.9875 - val_miss_rate: 0.6137 - val_fall_out: 0.0125 - val_mcc: 0.5150
Epoch 5/100
63/63 [==============================] - 1s 9ms/step - loss: 1.2735 - accuracy: 0.5605 - recall: 0.3590 - precision: 0.7300 - AUROC: 0.9093 - AUPRC: 0.6027 - f1_score: 0.4813 - balanced_accuracy: 0.6721 - specificity: 0.9852 - miss_rate: 0.6410 - fall_out: 0.0148 - mcc: 0.4776 - val_loss: 1.0071 - val_accuracy: 0.6473 - val_recall: 0.4469 - val_precision: 0.8139 - val_AUROC: 0.9465 - val_AUPRC: 0.7304 - val_f1_score: 0.5770 - val_balanced_accuracy: 0.7178 - val_specificity: 0.9886 - val_miss_rate: 0.5531 - val_fall_out: 0.0114 - val_mcc: 0.5736
Epoch 6/100
63/63 [==============================] - 1s 9ms/step - loss: 1.1877 - accuracy: 0.5848 - recall: 0.3964 - precision: 0.7440 - AUROC: 0.9217 - AUPRC: 0.6448 - f1_score: 0.5172 - balanced_accuracy: 0.6906 - specificity: 0.9848 - miss_rate: 0.6036 - fall_out: 0.0152 - mcc: 0.5093 - val_loss: 0.9173 - val_accuracy: 0.6979 - val_recall: 0.4990 - val_precision: 0.8527 - val_AUROC: 0.9565 - val_AUPRC: 0.7795 - val_f1_score: 0.6296 - val_balanced_accuracy: 0.7447 - val_specificity: 0.9904 - val_miss_rate: 0.5010 - val_fall_out: 0.0096 - val_mcc: 0.6255
Epoch 7/100
63/63 [==============================] - 1s 9ms/step - loss: 1.1040 - accuracy: 0.6200 - recall: 0.4465 - precision: 0.7627 - AUROC: 0.9319 - AUPRC: 0.6826 - f1_score: 0.5633 - balanced_accuracy: 0.7155 - specificity: 0.9846 - miss_rate: 0.5535 - fall_out: 0.0154 - mcc: 0.5509 - val_loss: 0.8600 - val_accuracy: 0.7019 - val_recall: 0.5586 - val_precision: 0.8284 - val_AUROC: 0.9593 - val_AUPRC: 0.7913 - val_f1_score: 0.6673 - val_balanced_accuracy: 0.7729 - val_specificity: 0.9871 - val_miss_rate: 0.4414 - val_fall_out: 0.0129 - val_mcc: 0.6529
Epoch 8/100
63/63 [==============================] - 1s 10ms/step - loss: 1.0265 - accuracy: 0.6497 - recall: 0.4994 - precision: 0.7810 - AUROC: 0.9399 - AUPRC: 0.7201 - f1_score: 0.6092 - balanced_accuracy: 0.7419 - specificity: 0.9844 - miss_rate: 0.5006 - fall_out: 0.0156 - mcc: 0.5933 - val_loss: 0.7934 - val_accuracy: 0.7355 - val_recall: 0.6017 - val_precision: 0.8494 - val_AUROC: 0.9653 - val_AUPRC: 0.8187 - val_f1_score: 0.7044 - val_balanced_accuracy: 0.7949 - val_specificity: 0.9881 - val_miss_rate: 0.3983 - val_fall_out: 0.0119 - val_mcc: 0.6897
Epoch 9/100
63/63 [==============================] - 1s 10ms/step - loss: 1.0055 - accuracy: 0.6662 - recall: 0.5157 - precision: 0.7837 - AUROC: 0.9425 - AUPRC: 0.7303 - f1_score: 0.6220 - balanced_accuracy: 0.7499 - specificity: 0.9842 - miss_rate: 0.4843 - fall_out: 0.0158 - mcc: 0.6048 - val_loss: 0.7525 - val_accuracy: 0.7465 - val_recall: 0.6182 - val_precision: 0.8690 - val_AUROC: 0.9698 - val_AUPRC: 0.8406 - val_f1_score: 0.7225 - val_balanced_accuracy: 0.8039 - val_specificity: 0.9896 - val_miss_rate: 0.3818 - val_fall_out: 0.0104 - val_mcc: 0.7094
Epoch 10/100
63/63 [==============================] - 1s 10ms/step - loss: 0.9443 - accuracy: 0.6824 - recall: 0.5458 - precision: 0.7955 - AUROC: 0.9488 - AUPRC: 0.7530 - f1_score: 0.6475 - balanced_accuracy: 0.7651 - specificity: 0.9844 - miss_rate: 0.4542 - fall_out: 0.0156 - mcc: 0.6293 - val_loss: 0.6981 - val_accuracy: 0.7625 - val_recall: 0.6648 - val_precision: 0.8696 - val_AUROC: 0.9728 - val_AUPRC: 0.8564 - val_f1_score: 0.7535 - val_balanced_accuracy: 0.8269 - val_specificity: 0.9889 - val_miss_rate: 0.3352 - val_fall_out: 0.0111 - val_mcc: 0.7381
Epoch 11/100
63/63 [==============================] - 1s 10ms/step - loss: 0.8954 - accuracy: 0.7057 - recall: 0.5812 - precision: 0.8021 - AUROC: 0.9538 - AUPRC: 0.7737 - f1_score: 0.6740 - balanced_accuracy: 0.7826 - specificity: 0.9841 - miss_rate: 0.4188 - fall_out: 0.0159 - mcc: 0.6541 - val_loss: 0.6723 - val_accuracy: 0.7751 - val_recall: 0.6839 - val_precision: 0.8789 - val_AUROC: 0.9752 - val_AUPRC: 0.8665 - val_f1_score: 0.7692 - val_balanced_accuracy: 0.8367 - val_specificity: 0.9895 - val_miss_rate: 0.3161 - val_fall_out: 0.0105 - val_mcc: 0.7542
Epoch 12/100
63/63 [==============================] - 1s 9ms/step - loss: 0.8533 - accuracy: 0.7221 - recall: 0.5999 - precision: 0.8173 - AUROC: 0.9575 - AUPRC: 0.7941 - f1_score: 0.6919 - balanced_accuracy: 0.7925 - specificity: 0.9851 - miss_rate: 0.4001 - fall_out: 0.0149 - mcc: 0.6730 - val_loss: 0.6343 - val_accuracy: 0.7851 - val_recall: 0.6914 - val_precision: 0.8751 - val_AUROC: 0.9773 - val_AUPRC: 0.8790 - val_f1_score: 0.7725 - val_balanced_accuracy: 0.8402 - val_specificity: 0.9890 - val_miss_rate: 0.3086 - val_fall_out: 0.0110 - val_mcc: 0.7567
Epoch 13/100
63/63 [==============================] - 1s 9ms/step - loss: 0.8229 - accuracy: 0.7355 - recall: 0.6207 - precision: 0.8222 - AUROC: 0.9600 - AUPRC: 0.8024 - f1_score: 0.7074 - balanced_accuracy: 0.8029 - specificity: 0.9851 - miss_rate: 0.3793 - fall_out: 0.0149 - mcc: 0.6879 - val_loss: 0.6170 - val_accuracy: 0.7926 - val_recall: 0.7069 - val_precision: 0.8731 - val_AUROC: 0.9783 - val_AUPRC: 0.8841 - val_f1_score: 0.7813 - val_balanced_accuracy: 0.8478 - val_specificity: 0.9886 - val_miss_rate: 0.2931 - val_fall_out: 0.0114 - val_mcc: 0.7649
Epoch 14/100
63/63 [==============================] - 1s 9ms/step - loss: 0.7981 - accuracy: 0.7377 - recall: 0.6348 - precision: 0.8218 - AUROC: 0.9624 - AUPRC: 0.8135 - f1_score: 0.7163 - balanced_accuracy: 0.8097 - specificity: 0.9847 - miss_rate: 0.3652 - fall_out: 0.0153 - mcc: 0.6961 - val_loss: 0.5995 - val_accuracy: 0.8056 - val_recall: 0.7179 - val_precision: 0.8818 - val_AUROC: 0.9801 - val_AUPRC: 0.8897 - val_f1_score: 0.7915 - val_balanced_accuracy: 0.8536 - val_specificity: 0.9893 - val_miss_rate: 0.2821 - val_fall_out: 0.0107 - val_mcc: 0.7759
Epoch 15/100
63/63 [==============================] - 1s 9ms/step - loss: 0.7582 - accuracy: 0.7513 - recall: 0.6574 - precision: 0.8328 - AUROC: 0.9658 - AUPRC: 0.8294 - f1_score: 0.7348 - balanced_accuracy: 0.8214 - specificity: 0.9853 - miss_rate: 0.3426 - fall_out: 0.0147 - mcc: 0.7151 - val_loss: 0.5666 - val_accuracy: 0.8146 - val_recall: 0.7345 - val_precision: 0.8805 - val_AUROC: 0.9821 - val_AUPRC: 0.8992 - val_f1_score: 0.8009 - val_balanced_accuracy: 0.8617 - val_specificity: 0.9889 - val_miss_rate: 0.2655 - val_fall_out: 0.0111 - val_mcc: 0.7848
Epoch 16/100
63/63 [==============================] - 1s 9ms/step - loss: 0.7189 - accuracy: 0.7633 - recall: 0.6776 - precision: 0.8402 - AUROC: 0.9691 - AUPRC: 0.8422 - f1_score: 0.7502 - balanced_accuracy: 0.8316 - specificity: 0.9857 - miss_rate: 0.3224 - fall_out: 0.0143 - mcc: 0.7308 - val_loss: 0.5434 - val_accuracy: 0.8161 - val_recall: 0.7510 - val_precision: 0.8854 - val_AUROC: 0.9835 - val_AUPRC: 0.9056 - val_f1_score: 0.8127 - val_balanced_accuracy: 0.8701 - val_specificity: 0.9892 - val_miss_rate: 0.2490 - val_fall_out: 0.0108 - val_mcc: 0.7970
Epoch 17/100
63/63 [==============================] - 1s 9ms/step - loss: 0.6974 - accuracy: 0.7720 - recall: 0.6954 - precision: 0.8460 - AUROC: 0.9705 - AUPRC: 0.8513 - f1_score: 0.7633 - balanced_accuracy: 0.8407 - specificity: 0.9859 - miss_rate: 0.3046 - fall_out: 0.0141 - mcc: 0.7441 - val_loss: 0.5375 - val_accuracy: 0.8166 - val_recall: 0.7550 - val_precision: 0.8823 - val_AUROC: 0.9839 - val_AUPRC: 0.9070 - val_f1_score: 0.8137 - val_balanced_accuracy: 0.8719 - val_specificity: 0.9888 - val_miss_rate: 0.2450 - val_fall_out: 0.0112 - val_mcc: 0.7977
Epoch 18/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6735 - accuracy: 0.7858 - recall: 0.7083 - precision: 0.8549 - AUROC: 0.9723 - AUPRC: 0.8596 - f1_score: 0.7747 - balanced_accuracy: 0.8475 - specificity: 0.9866 - miss_rate: 0.2917 - fall_out: 0.0134 - mcc: 0.7563 - val_loss: 0.5066 - val_accuracy: 0.8302 - val_recall: 0.7806 - val_precision: 0.8852 - val_AUROC: 0.9846 - val_AUPRC: 0.9147 - val_f1_score: 0.8296 - val_balanced_accuracy: 0.8847 - val_specificity: 0.9888 - val_miss_rate: 0.2194 - val_fall_out: 0.0112 - val_mcc: 0.8139
Epoch 19/100
63/63 [==============================] - 1s 9ms/step - loss: 0.6501 - accuracy: 0.7832 - recall: 0.7104 - precision: 0.8456 - AUROC: 0.9741 - AUPRC: 0.8642 - f1_score: 0.7721 - balanced_accuracy: 0.8480 - specificity: 0.9856 - miss_rate: 0.2896 - fall_out: 0.0144 - mcc: 0.7527 - val_loss: 0.4922 - val_accuracy: 0.8317 - val_recall: 0.7821 - val_precision: 0.8869 - val_AUROC: 0.9854 - val_AUPRC: 0.9182 - val_f1_score: 0.8312 - val_balanced_accuracy: 0.8855 - val_specificity: 0.9889 - val_miss_rate: 0.2179 - val_fall_out: 0.0111 - val_mcc: 0.8157
Epoch 20/100
63/63 [==============================] - 1s 9ms/step - loss: 0.6211 - accuracy: 0.7955 - recall: 0.7328 - precision: 0.8553 - AUROC: 0.9759 - AUPRC: 0.8731 - f1_score: 0.7893 - balanced_accuracy: 0.8595 - specificity: 0.9862 - miss_rate: 0.2672 - fall_out: 0.0138 - mcc: 0.7707 - val_loss: 0.4682 - val_accuracy: 0.8452 - val_recall: 0.7971 - val_precision: 0.8923 - val_AUROC: 0.9867 - val_AUPRC: 0.9254 - val_f1_score: 0.8420 - val_balanced_accuracy: 0.8932 - val_specificity: 0.9893 - val_miss_rate: 0.2029 - val_fall_out: 0.0107 - val_mcc: 0.8272
Epoch 21/100
63/63 [==============================] - 1s 9ms/step - loss: 0.6121 - accuracy: 0.7996 - recall: 0.7381 - precision: 0.8580 - AUROC: 0.9764 - AUPRC: 0.8781 - f1_score: 0.7936 - balanced_accuracy: 0.8623 - specificity: 0.9864 - miss_rate: 0.2619 - fall_out: 0.0136 - mcc: 0.7752 - val_loss: 0.4643 - val_accuracy: 0.8497 - val_recall: 0.7946 - val_precision: 0.8976 - val_AUROC: 0.9863 - val_AUPRC: 0.9260 - val_f1_score: 0.8429 - val_balanced_accuracy: 0.8923 - val_specificity: 0.9899 - val_miss_rate: 0.2054 - val_fall_out: 0.0101 - val_mcc: 0.8285
Epoch 22/100
63/63 [==============================] - 1s 9ms/step - loss: 0.5889 - accuracy: 0.8101 - recall: 0.7484 - precision: 0.8672 - AUROC: 0.9778 - AUPRC: 0.8864 - f1_score: 0.8034 - balanced_accuracy: 0.8678 - specificity: 0.9873 - miss_rate: 0.2516 - fall_out: 0.0127 - mcc: 0.7859 - val_loss: 0.4474 - val_accuracy: 0.8517 - val_recall: 0.7986 - val_precision: 0.9006 - val_AUROC: 0.9880 - val_AUPRC: 0.9314 - val_f1_score: 0.8465 - val_balanced_accuracy: 0.8944 - val_specificity: 0.9902 - val_miss_rate: 0.2014 - val_fall_out: 0.0098 - val_mcc: 0.8324
Epoch 23/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5781 - accuracy: 0.8146 - recall: 0.7576 - precision: 0.8669 - AUROC: 0.9789 - AUPRC: 0.8893 - f1_score: 0.8086 - balanced_accuracy: 0.8724 - specificity: 0.9871 - miss_rate: 0.2424 - fall_out: 0.0129 - mcc: 0.7911 - val_loss: 0.4430 - val_accuracy: 0.8567 - val_recall: 0.8036 - val_precision: 0.9001 - val_AUROC: 0.9883 - val_AUPRC: 0.9326 - val_f1_score: 0.8491 - val_balanced_accuracy: 0.8968 - val_specificity: 0.9901 - val_miss_rate: 0.1964 - val_fall_out: 0.0099 - val_mcc: 0.8350
Epoch 24/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5607 - accuracy: 0.8168 - recall: 0.7634 - precision: 0.8726 - AUROC: 0.9799 - AUPRC: 0.8949 - f1_score: 0.8143 - balanced_accuracy: 0.8755 - specificity: 0.9876 - miss_rate: 0.2366 - fall_out: 0.0124 - mcc: 0.7974 - val_loss: 0.4263 - val_accuracy: 0.8642 - val_recall: 0.8176 - val_precision: 0.9027 - val_AUROC: 0.9889 - val_AUPRC: 0.9359 - val_f1_score: 0.8580 - val_balanced_accuracy: 0.9039 - val_specificity: 0.9902 - val_miss_rate: 0.1824 - val_fall_out: 0.0098 - val_mcc: 0.8444
Epoch 25/100
63/63 [==============================] - 1s 9ms/step - loss: 0.5436 - accuracy: 0.8221 - recall: 0.7729 - precision: 0.8777 - AUROC: 0.9806 - AUPRC: 0.9009 - f1_score: 0.8220 - balanced_accuracy: 0.8805 - specificity: 0.9880 - miss_rate: 0.2271 - fall_out: 0.0120 - mcc: 0.8056 - val_loss: 0.4240 - val_accuracy: 0.8612 - val_recall: 0.8191 - val_precision: 0.9018 - val_AUROC: 0.9882 - val_AUPRC: 0.9359 - val_f1_score: 0.8585 - val_balanced_accuracy: 0.9046 - val_specificity: 0.9901 - val_miss_rate: 0.1809 - val_fall_out: 0.0099 - val_mcc: 0.8448
Epoch 26/100
63/63 [==============================] - 1s 9ms/step - loss: 0.5280 - accuracy: 0.8280 - recall: 0.7782 - precision: 0.8770 - AUROC: 0.9820 - AUPRC: 0.9051 - f1_score: 0.8247 - balanced_accuracy: 0.8830 - specificity: 0.9879 - miss_rate: 0.2218 - fall_out: 0.0121 - mcc: 0.8082 - val_loss: 0.4094 - val_accuracy: 0.8677 - val_recall: 0.8302 - val_precision: 0.9055 - val_AUROC: 0.9892 - val_AUPRC: 0.9398 - val_f1_score: 0.8662 - val_balanced_accuracy: 0.9103 - val_specificity: 0.9904 - val_miss_rate: 0.1698 - val_fall_out: 0.0096 - val_mcc: 0.8530
Epoch 27/100
63/63 [==============================] - 1s 9ms/step - loss: 0.5176 - accuracy: 0.8340 - recall: 0.7857 - precision: 0.8789 - AUROC: 0.9827 - AUPRC: 0.9081 - f1_score: 0.8297 - balanced_accuracy: 0.8868 - specificity: 0.9880 - miss_rate: 0.2143 - fall_out: 0.0120 - mcc: 0.8135 - val_loss: 0.4060 - val_accuracy: 0.8737 - val_recall: 0.8322 - val_precision: 0.9027 - val_AUROC: 0.9894 - val_AUPRC: 0.9411 - val_f1_score: 0.8660 - val_balanced_accuracy: 0.9111 - val_specificity: 0.9900 - val_miss_rate: 0.1678 - val_fall_out: 0.0100 - val_mcc: 0.8527
Epoch 28/100
63/63 [==============================] - 1s 9ms/step - loss: 0.4957 - accuracy: 0.8419 - recall: 0.7965 - precision: 0.8842 - AUROC: 0.9839 - AUPRC: 0.9140 - f1_score: 0.8380 - balanced_accuracy: 0.8924 - specificity: 0.9884 - miss_rate: 0.2035 - fall_out: 0.0116 - mcc: 0.8224 - val_loss: 0.3980 - val_accuracy: 0.8667 - val_recall: 0.8367 - val_precision: 0.9027 - val_AUROC: 0.9898 - val_AUPRC: 0.9435 - val_f1_score: 0.8684 - val_balanced_accuracy: 0.9133 - val_specificity: 0.9900 - val_miss_rate: 0.1633 - val_fall_out: 0.0100 - val_mcc: 0.8552
Epoch 29/100
63/63 [==============================] - 1s 9ms/step - loss: 0.4741 - accuracy: 0.8439 - recall: 0.8014 - precision: 0.8882 - AUROC: 0.9852 - AUPRC: 0.9182 - f1_score: 0.8426 - balanced_accuracy: 0.8951 - specificity: 0.9888 - miss_rate: 0.1986 - fall_out: 0.0112 - mcc: 0.8274 - val_loss: 0.3860 - val_accuracy: 0.8747 - val_recall: 0.8527 - val_precision: 0.9111 - val_AUROC: 0.9900 - val_AUPRC: 0.9456 - val_f1_score: 0.8810 - val_balanced_accuracy: 0.9217 - val_specificity: 0.9908 - val_miss_rate: 0.1473 - val_fall_out: 0.0092 - val_mcc: 0.8688
Epoch 30/100
63/63 [==============================] - 1s 9ms/step - loss: 0.4940 - accuracy: 0.8443 - recall: 0.7971 - precision: 0.8845 - AUROC: 0.9835 - AUPRC: 0.9137 - f1_score: 0.8385 - balanced_accuracy: 0.8928 - specificity: 0.9884 - miss_rate: 0.2029 - fall_out: 0.0116 - mcc: 0.8230 - val_loss: 0.3823 - val_accuracy: 0.8768 - val_recall: 0.8362 - val_precision: 0.9105 - val_AUROC: 0.9907 - val_AUPRC: 0.9466 - val_f1_score: 0.8718 - val_balanced_accuracy: 0.9135 - val_specificity: 0.9909 - val_miss_rate: 0.1638 - val_fall_out: 0.0091 - val_mcc: 0.8591
Epoch 31/100
63/63 [==============================] - 1s 9ms/step - loss: 0.4624 - accuracy: 0.8562 - recall: 0.8164 - precision: 0.8931 - AUROC: 0.9854 - AUPRC: 0.9232 - f1_score: 0.8530 - balanced_accuracy: 0.9028 - specificity: 0.9891 - miss_rate: 0.1836 - fall_out: 0.0109 - mcc: 0.8385 - val_loss: 0.3798 - val_accuracy: 0.8737 - val_recall: 0.8472 - val_precision: 0.9019 - val_AUROC: 0.9908 - val_AUPRC: 0.9472 - val_f1_score: 0.8737 - val_balanced_accuracy: 0.9185 - val_specificity: 0.9898 - val_miss_rate: 0.1528 - val_fall_out: 0.0102 - val_mcc: 0.8606
Epoch 32/100
63/63 [==============================] - 1s 9ms/step - loss: 0.4484 - accuracy: 0.8582 - recall: 0.8170 - precision: 0.8958 - AUROC: 0.9864 - AUPRC: 0.9258 - f1_score: 0.8546 - balanced_accuracy: 0.9032 - specificity: 0.9894 - miss_rate: 0.1830 - fall_out: 0.0106 - mcc: 0.8403 - val_loss: 0.3654 - val_accuracy: 0.8783 - val_recall: 0.8557 - val_precision: 0.9061 - val_AUROC: 0.9912 - val_AUPRC: 0.9506 - val_f1_score: 0.8802 - val_balanced_accuracy: 0.9229 - val_specificity: 0.9901 - val_miss_rate: 0.1443 - val_fall_out: 0.0099 - val_mcc: 0.8677
Epoch 33/100
63/63 [==============================] - 1s 9ms/step - loss: 0.4283 - accuracy: 0.8635 - recall: 0.8264 - precision: 0.8961 - AUROC: 0.9873 - AUPRC: 0.9310 - f1_score: 0.8598 - balanced_accuracy: 0.9079 - specificity: 0.9894 - miss_rate: 0.1736 - fall_out: 0.0106 - mcc: 0.8458 - val_loss: 0.3661 - val_accuracy: 0.8838 - val_recall: 0.8567 - val_precision: 0.9072 - val_AUROC: 0.9904 - val_AUPRC: 0.9489 - val_f1_score: 0.8812 - val_balanced_accuracy: 0.9235 - val_specificity: 0.9903 - val_miss_rate: 0.1433 - val_fall_out: 0.0097 - val_mcc: 0.8689
Epoch 34/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4277 - accuracy: 0.8651 - recall: 0.8332 - precision: 0.9003 - AUROC: 0.9873 - AUPRC: 0.9316 - f1_score: 0.8654 - balanced_accuracy: 0.9115 - specificity: 0.9897 - miss_rate: 0.1668 - fall_out: 0.0103 - mcc: 0.8519 - val_loss: 0.3507 - val_accuracy: 0.8873 - val_recall: 0.8642 - val_precision: 0.9146 - val_AUROC: 0.9916 - val_AUPRC: 0.9538 - val_f1_score: 0.8887 - val_balanced_accuracy: 0.9276 - val_specificity: 0.9910 - val_miss_rate: 0.1358 - val_fall_out: 0.0090 - val_mcc: 0.8772
Epoch 35/100
63/63 [==============================] - 1s 9ms/step - loss: 0.4215 - accuracy: 0.8646 - recall: 0.8337 - precision: 0.8981 - AUROC: 0.9873 - AUPRC: 0.9329 - f1_score: 0.8647 - balanced_accuracy: 0.9116 - specificity: 0.9895 - miss_rate: 0.1663 - fall_out: 0.0105 - mcc: 0.8510 - val_loss: 0.3659 - val_accuracy: 0.8818 - val_recall: 0.8532 - val_precision: 0.9044 - val_AUROC: 0.9909 - val_AUPRC: 0.9506 - val_f1_score: 0.8781 - val_balanced_accuracy: 0.9216 - val_specificity: 0.9900 - val_miss_rate: 0.1468 - val_fall_out: 0.0100 - val_mcc: 0.8654
Epoch 36/100
63/63 [==============================] - 1s 9ms/step - loss: 0.4120 - accuracy: 0.8686 - recall: 0.8348 - precision: 0.9024 - AUROC: 0.9884 - AUPRC: 0.9357 - f1_score: 0.8673 - balanced_accuracy: 0.9124 - specificity: 0.9900 - miss_rate: 0.1652 - fall_out: 0.0100 - mcc: 0.8540 - val_loss: 0.3400 - val_accuracy: 0.8888 - val_recall: 0.8642 - val_precision: 0.9117 - val_AUROC: 0.9924 - val_AUPRC: 0.9560 - val_f1_score: 0.8873 - val_balanced_accuracy: 0.9275 - val_specificity: 0.9907 - val_miss_rate: 0.1358 - val_fall_out: 0.0093 - val_mcc: 0.8756
Epoch 37/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3979 - accuracy: 0.8749 - recall: 0.8437 - precision: 0.9049 - AUROC: 0.9891 - AUPRC: 0.9394 - f1_score: 0.8732 - balanced_accuracy: 0.9169 - specificity: 0.9901 - miss_rate: 0.1563 - fall_out: 0.0099 - mcc: 0.8603 - val_loss: 0.3424 - val_accuracy: 0.8873 - val_recall: 0.8662 - val_precision: 0.9071 - val_AUROC: 0.9917 - val_AUPRC: 0.9561 - val_f1_score: 0.8862 - val_balanced_accuracy: 0.9282 - val_specificity: 0.9901 - val_miss_rate: 0.1338 - val_fall_out: 0.0099 - val_mcc: 0.8742
Epoch 38/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3952 - accuracy: 0.8758 - recall: 0.8454 - precision: 0.9019 - AUROC: 0.9882 - AUPRC: 0.9404 - f1_score: 0.8728 - balanced_accuracy: 0.9176 - specificity: 0.9898 - miss_rate: 0.1546 - fall_out: 0.0102 - mcc: 0.8597 - val_loss: 0.3344 - val_accuracy: 0.8893 - val_recall: 0.8682 - val_precision: 0.9155 - val_AUROC: 0.9919 - val_AUPRC: 0.9573 - val_f1_score: 0.8912 - val_balanced_accuracy: 0.9297 - val_specificity: 0.9911 - val_miss_rate: 0.1318 - val_fall_out: 0.0089 - val_mcc: 0.8799
Epoch 39/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3705 - accuracy: 0.8835 - recall: 0.8543 - precision: 0.9108 - AUROC: 0.9901 - AUPRC: 0.9462 - f1_score: 0.8817 - balanced_accuracy: 0.9225 - specificity: 0.9907 - miss_rate: 0.1457 - fall_out: 0.0093 - mcc: 0.8695 - val_loss: 0.3366 - val_accuracy: 0.8933 - val_recall: 0.8758 - val_precision: 0.9109 - val_AUROC: 0.9921 - val_AUPRC: 0.9564 - val_f1_score: 0.8930 - val_balanced_accuracy: 0.9331 - val_specificity: 0.9905 - val_miss_rate: 0.1242 - val_fall_out: 0.0095 - val_mcc: 0.8816
Epoch 40/100
63/63 [==============================] - 1s 9ms/step - loss: 0.3854 - accuracy: 0.8794 - recall: 0.8522 - precision: 0.9099 - AUROC: 0.9889 - AUPRC: 0.9418 - f1_score: 0.8801 - balanced_accuracy: 0.9214 - specificity: 0.9906 - miss_rate: 0.1478 - fall_out: 0.0094 - mcc: 0.8678 - val_loss: 0.3253 - val_accuracy: 0.8893 - val_recall: 0.8712 - val_precision: 0.9148 - val_AUROC: 0.9929 - val_AUPRC: 0.9588 - val_f1_score: 0.8925 - val_balanced_accuracy: 0.9311 - val_specificity: 0.9910 - val_miss_rate: 0.1288 - val_fall_out: 0.0090 - val_mcc: 0.8812
Epoch 41/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3802 - accuracy: 0.8783 - recall: 0.8525 - precision: 0.9070 - AUROC: 0.9896 - AUPRC: 0.9435 - f1_score: 0.8789 - balanced_accuracy: 0.9214 - specificity: 0.9903 - miss_rate: 0.1475 - fall_out: 0.0097 - mcc: 0.8664 - val_loss: 0.3177 - val_accuracy: 0.8953 - val_recall: 0.8702 - val_precision: 0.9166 - val_AUROC: 0.9935 - val_AUPRC: 0.9611 - val_f1_score: 0.8928 - val_balanced_accuracy: 0.9307 - val_specificity: 0.9912 - val_miss_rate: 0.1298 - val_fall_out: 0.0088 - val_mcc: 0.8816
Epoch 42/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3549 - accuracy: 0.8851 - recall: 0.8580 - precision: 0.9125 - AUROC: 0.9910 - AUPRC: 0.9500 - f1_score: 0.8844 - balanced_accuracy: 0.9244 - specificity: 0.9909 - miss_rate: 0.1420 - fall_out: 0.0091 - mcc: 0.8725 - val_loss: 0.3268 - val_accuracy: 0.8943 - val_recall: 0.8783 - val_precision: 0.9164 - val_AUROC: 0.9925 - val_AUPRC: 0.9589 - val_f1_score: 0.8969 - val_balanced_accuracy: 0.9347 - val_specificity: 0.9911 - val_miss_rate: 0.1217 - val_fall_out: 0.0089 - val_mcc: 0.8860
Epoch 43/100
63/63 [==============================] - 1s 9ms/step - loss: 0.3519 - accuracy: 0.8873 - recall: 0.8603 - precision: 0.9116 - AUROC: 0.9908 - AUPRC: 0.9496 - f1_score: 0.8852 - balanced_accuracy: 0.9255 - specificity: 0.9907 - miss_rate: 0.1397 - fall_out: 0.0093 - mcc: 0.8733 - val_loss: 0.3185 - val_accuracy: 0.8968 - val_recall: 0.8813 - val_precision: 0.9166 - val_AUROC: 0.9924 - val_AUPRC: 0.9599 - val_f1_score: 0.8986 - val_balanced_accuracy: 0.9362 - val_specificity: 0.9911 - val_miss_rate: 0.1187 - val_fall_out: 0.0089 - val_mcc: 0.8878
250/250 [==============================] - 1s 4ms/step - loss: 0.1109 - accuracy: 0.9676 - recall: 0.9589 - precision: 0.9783 - AUROC: 0.9993 - AUPRC: 0.9949 - f1_score: 0.9685 - balanced_accuracy: 0.9783 - specificity: 0.9976 - miss_rate: 0.0411 - fall_out: 0.0024 - mcc: 0.9651
63/63 [==============================] - 0s 4ms/step - loss: 0.3185 - accuracy: 0.8968 - recall: 0.8813 - precision: 0.9161 - AUROC: 0.9924 - AUPRC: 0.9599 - f1_score: 0.8984 - balanced_accuracy: 0.9362 - specificity: 0.9910 - miss_rate: 0.1187 - fall_out: 0.0090 - mcc: 0.8875
4it [02:10, 32.02s/it]
-- HOLDOUT 5 -- WINDOW window_3s
-- 5 Uncorrelated features: [Pearson+Spearman]
['mfcc16_mean', 'tempo', 'mfcc10_var', 'mfcc19_mean', 'mfcc17_mean']
-- Actually uncorrelated features: [confirmed via MIC]
[]
-- 1 Correlated features: [Pearson]
['spectral_centroid_mean']
Model: "MLP"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_240 (Dense) (None, 256) 14592
dropout_186 (Dropout) (None, 256) 0
dense_241 (Dense) (None, 256) 65792
dropout_187 (Dropout) (None, 256) 0
dense_242 (Dense) (None, 128) 32896
dropout_188 (Dropout) (None, 128) 0
dense_243 (Dense) (None, 128) 16512
dropout_189 (Dropout) (None, 128) 0
dense_244 (Dense) (None, 10) 1290
=================================================================
Total params: 131,082
Trainable params: 131,082
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 2s 18ms/step - loss: 2.3050 - accuracy: 0.1816 - recall: 0.0083 - precision: 0.2619 - AUROC: 0.6176 - AUPRC: 0.1565 - f1_score: 0.0160 - balanced_accuracy: 0.5028 - specificity: 0.9974 - miss_rate: 0.9917 - fall_out: 0.0026 - mcc: 0.0304 - val_loss: 1.8621 - val_accuracy: 0.3788 - val_recall: 0.0406 - val_precision: 0.8020 - val_AUROC: 0.8258 - val_AUPRC: 0.3941 - val_f1_score: 0.0773 - val_balanced_accuracy: 0.5197 - val_specificity: 0.9989 - val_miss_rate: 0.9594 - val_fall_out: 0.0011 - val_mcc: 0.1669
Epoch 2/100
63/63 [==============================] - 1s 9ms/step - loss: 1.8407 - accuracy: 0.3456 - recall: 0.1197 - precision: 0.6216 - AUROC: 0.7980 - AUPRC: 0.3484 - f1_score: 0.2008 - balanced_accuracy: 0.5558 - specificity: 0.9919 - miss_rate: 0.8803 - fall_out: 0.0081 - mcc: 0.2437 - val_loss: 1.4823 - val_accuracy: 0.4739 - val_recall: 0.2149 - val_precision: 0.7989 - val_AUROC: 0.8951 - val_AUPRC: 0.5387 - val_f1_score: 0.3387 - val_balanced_accuracy: 0.6045 - val_specificity: 0.9940 - val_miss_rate: 0.7851 - val_fall_out: 0.0060 - val_mcc: 0.3874
Epoch 3/100
63/63 [==============================] - 1s 10ms/step - loss: 1.5851 - accuracy: 0.4320 - recall: 0.2089 - precision: 0.6567 - AUROC: 0.8589 - AUPRC: 0.4583 - f1_score: 0.3170 - balanced_accuracy: 0.5984 - specificity: 0.9879 - miss_rate: 0.7911 - fall_out: 0.0121 - mcc: 0.3364 - val_loss: 1.2454 - val_accuracy: 0.5511 - val_recall: 0.2986 - val_precision: 0.8087 - val_AUROC: 0.9215 - val_AUPRC: 0.6325 - val_f1_score: 0.4362 - val_balanced_accuracy: 0.6454 - val_specificity: 0.9922 - val_miss_rate: 0.7014 - val_fall_out: 0.0078 - val_mcc: 0.4625
Epoch 4/100
63/63 [==============================] - 1s 9ms/step - loss: 1.4044 - accuracy: 0.5001 - recall: 0.2887 - precision: 0.7038 - AUROC: 0.8898 - AUPRC: 0.5402 - f1_score: 0.4095 - balanced_accuracy: 0.6376 - specificity: 0.9865 - miss_rate: 0.7113 - fall_out: 0.0135 - mcc: 0.4163 - val_loss: 1.1356 - val_accuracy: 0.5897 - val_recall: 0.3567 - val_precision: 0.7781 - val_AUROC: 0.9334 - val_AUPRC: 0.6700 - val_f1_score: 0.4892 - val_balanced_accuracy: 0.6727 - val_specificity: 0.9887 - val_miss_rate: 0.6433 - val_fall_out: 0.0113 - val_mcc: 0.4955
Epoch 5/100
63/63 [==============================] - 1s 10ms/step - loss: 1.2944 - accuracy: 0.5537 - recall: 0.3438 - precision: 0.7235 - AUROC: 0.9068 - AUPRC: 0.5928 - f1_score: 0.4661 - balanced_accuracy: 0.6646 - specificity: 0.9854 - miss_rate: 0.6562 - fall_out: 0.0146 - mcc: 0.4642 - val_loss: 1.0235 - val_accuracy: 0.6478 - val_recall: 0.4469 - val_precision: 0.8244 - val_AUROC: 0.9452 - val_AUPRC: 0.7270 - val_f1_score: 0.5796 - val_balanced_accuracy: 0.7182 - val_specificity: 0.9894 - val_miss_rate: 0.5531 - val_fall_out: 0.0106 - val_mcc: 0.5781
Epoch 6/100
63/63 [==============================] - 1s 10ms/step - loss: 1.2066 - accuracy: 0.5827 - recall: 0.3878 - precision: 0.7357 - AUROC: 0.9195 - AUPRC: 0.6351 - f1_score: 0.5079 - balanced_accuracy: 0.6862 - specificity: 0.9845 - miss_rate: 0.6122 - fall_out: 0.0155 - mcc: 0.4999 - val_loss: 0.9356 - val_accuracy: 0.6814 - val_recall: 0.4905 - val_precision: 0.8484 - val_AUROC: 0.9542 - val_AUPRC: 0.7669 - val_f1_score: 0.6216 - val_balanced_accuracy: 0.7404 - val_specificity: 0.9903 - val_miss_rate: 0.5095 - val_fall_out: 0.0097 - val_mcc: 0.6179
Epoch 7/100
63/63 [==============================] - 1s 9ms/step - loss: 1.1421 - accuracy: 0.6053 - recall: 0.4331 - precision: 0.7550 - AUROC: 0.9271 - AUPRC: 0.6683 - f1_score: 0.5505 - balanced_accuracy: 0.7088 - specificity: 0.9844 - miss_rate: 0.5669 - fall_out: 0.0156 - mcc: 0.5386 - val_loss: 0.8753 - val_accuracy: 0.7049 - val_recall: 0.5346 - val_precision: 0.8577 - val_AUROC: 0.9601 - val_AUPRC: 0.7921 - val_f1_score: 0.6586 - val_balanced_accuracy: 0.7624 - val_specificity: 0.9901 - val_miss_rate: 0.4654 - val_fall_out: 0.0099 - val_mcc: 0.6512
Epoch 8/100
63/63 [==============================] - 1s 10ms/step - loss: 1.0494 - accuracy: 0.6428 - recall: 0.4866 - precision: 0.7738 - AUROC: 0.9385 - AUPRC: 0.7094 - f1_score: 0.5975 - balanced_accuracy: 0.7354 - specificity: 0.9842 - miss_rate: 0.5134 - fall_out: 0.0158 - mcc: 0.5818 - val_loss: 0.8297 - val_accuracy: 0.7124 - val_recall: 0.5746 - val_precision: 0.8354 - val_AUROC: 0.9628 - val_AUPRC: 0.8045 - val_f1_score: 0.6809 - val_balanced_accuracy: 0.7810 - val_specificity: 0.9874 - val_miss_rate: 0.4254 - val_fall_out: 0.0126 - val_mcc: 0.6662
Epoch 9/100
63/63 [==============================] - 1s 10ms/step - loss: 0.9915 - accuracy: 0.6721 - recall: 0.5205 - precision: 0.7855 - AUROC: 0.9438 - AUPRC: 0.7354 - f1_score: 0.6261 - balanced_accuracy: 0.7524 - specificity: 0.9842 - miss_rate: 0.4795 - fall_out: 0.0158 - mcc: 0.6087 - val_loss: 0.7759 - val_accuracy: 0.7320 - val_recall: 0.5982 - val_precision: 0.8529 - val_AUROC: 0.9681 - val_AUPRC: 0.8276 - val_f1_score: 0.7032 - val_balanced_accuracy: 0.7934 - val_specificity: 0.9885 - val_miss_rate: 0.4018 - val_fall_out: 0.0115 - val_mcc: 0.6892
Epoch 10/100
63/63 [==============================] - 1s 10ms/step - loss: 0.9553 - accuracy: 0.6795 - recall: 0.5452 - precision: 0.7913 - AUROC: 0.9475 - AUPRC: 0.7472 - f1_score: 0.6456 - balanced_accuracy: 0.7646 - specificity: 0.9840 - miss_rate: 0.4548 - fall_out: 0.0160 - mcc: 0.6269 - val_loss: 0.7449 - val_accuracy: 0.7515 - val_recall: 0.6258 - val_precision: 0.8572 - val_AUROC: 0.9698 - val_AUPRC: 0.8387 - val_f1_score: 0.7234 - val_balanced_accuracy: 0.8071 - val_specificity: 0.9884 - val_miss_rate: 0.3742 - val_fall_out: 0.0116 - val_mcc: 0.7083
Epoch 11/100
63/63 [==============================] - 1s 10ms/step - loss: 0.9048 - accuracy: 0.6906 - recall: 0.5733 - precision: 0.8048 - AUROC: 0.9532 - AUPRC: 0.7714 - f1_score: 0.6696 - balanced_accuracy: 0.7789 - specificity: 0.9846 - miss_rate: 0.4267 - fall_out: 0.0154 - mcc: 0.6506 - val_loss: 0.7075 - val_accuracy: 0.7605 - val_recall: 0.6468 - val_precision: 0.8670 - val_AUROC: 0.9728 - val_AUPRC: 0.8514 - val_f1_score: 0.7409 - val_balanced_accuracy: 0.8179 - val_specificity: 0.9890 - val_miss_rate: 0.3532 - val_fall_out: 0.0110 - val_mcc: 0.7259
Epoch 12/100
63/63 [==============================] - 1s 10ms/step - loss: 0.8821 - accuracy: 0.7057 - recall: 0.5850 - precision: 0.8073 - AUROC: 0.9551 - AUPRC: 0.7820 - f1_score: 0.6784 - balanced_accuracy: 0.7848 - specificity: 0.9845 - miss_rate: 0.4150 - fall_out: 0.0155 - mcc: 0.6590 - val_loss: 0.6915 - val_accuracy: 0.7685 - val_recall: 0.6578 - val_precision: 0.8672 - val_AUROC: 0.9734 - val_AUPRC: 0.8576 - val_f1_score: 0.7481 - val_balanced_accuracy: 0.8233 - val_specificity: 0.9888 - val_miss_rate: 0.3422 - val_fall_out: 0.0112 - val_mcc: 0.7327
Epoch 13/100
63/63 [==============================] - 1s 10ms/step - loss: 0.8385 - accuracy: 0.7234 - recall: 0.6155 - precision: 0.8141 - AUROC: 0.9588 - AUPRC: 0.7991 - f1_score: 0.7010 - balanced_accuracy: 0.7999 - specificity: 0.9844 - miss_rate: 0.3845 - fall_out: 0.0156 - mcc: 0.6807 - val_loss: 0.6681 - val_accuracy: 0.7710 - val_recall: 0.6809 - val_precision: 0.8678 - val_AUROC: 0.9749 - val_AUPRC: 0.8643 - val_f1_score: 0.7631 - val_balanced_accuracy: 0.8347 - val_specificity: 0.9885 - val_miss_rate: 0.3191 - val_fall_out: 0.0115 - val_mcc: 0.7468
Epoch 14/100
63/63 [==============================] - 1s 10ms/step - loss: 0.8039 - accuracy: 0.7342 - recall: 0.6301 - precision: 0.8253 - AUROC: 0.9622 - AUPRC: 0.8103 - f1_score: 0.7146 - balanced_accuracy: 0.8077 - specificity: 0.9852 - miss_rate: 0.3699 - fall_out: 0.0148 - mcc: 0.6951 - val_loss: 0.6431 - val_accuracy: 0.7911 - val_recall: 0.6964 - val_precision: 0.8726 - val_AUROC: 0.9765 - val_AUPRC: 0.8724 - val_f1_score: 0.7746 - val_balanced_accuracy: 0.8425 - val_specificity: 0.9887 - val_miss_rate: 0.3036 - val_fall_out: 0.0113 - val_mcc: 0.7584
Epoch 15/100
63/63 [==============================] - 1s 10ms/step - loss: 0.7620 - accuracy: 0.7509 - recall: 0.6541 - precision: 0.8350 - AUROC: 0.9656 - AUPRC: 0.8278 - f1_score: 0.7335 - balanced_accuracy: 0.8198 - specificity: 0.9856 - miss_rate: 0.3459 - fall_out: 0.0144 - mcc: 0.7142 - val_loss: 0.6213 - val_accuracy: 0.7921 - val_recall: 0.7049 - val_precision: 0.8590 - val_AUROC: 0.9784 - val_AUPRC: 0.8783 - val_f1_score: 0.7744 - val_balanced_accuracy: 0.8460 - val_specificity: 0.9871 - val_miss_rate: 0.2951 - val_fall_out: 0.0129 - val_mcc: 0.7564
Epoch 16/100
63/63 [==============================] - 1s 10ms/step - loss: 0.7255 - accuracy: 0.7590 - recall: 0.6760 - precision: 0.8405 - AUROC: 0.9683 - AUPRC: 0.8395 - f1_score: 0.7493 - balanced_accuracy: 0.8309 - specificity: 0.9857 - miss_rate: 0.3240 - fall_out: 0.0143 - mcc: 0.7300 - val_loss: 0.5982 - val_accuracy: 0.7886 - val_recall: 0.7290 - val_precision: 0.8620 - val_AUROC: 0.9791 - val_AUPRC: 0.8848 - val_f1_score: 0.7899 - val_balanced_accuracy: 0.8580 - val_specificity: 0.9870 - val_miss_rate: 0.2710 - val_fall_out: 0.0130 - val_mcc: 0.7720
Epoch 17/100
63/63 [==============================] - 1s 9ms/step - loss: 0.6887 - accuracy: 0.7739 - recall: 0.6946 - precision: 0.8452 - AUROC: 0.9715 - AUPRC: 0.8510 - f1_score: 0.7625 - balanced_accuracy: 0.8402 - specificity: 0.9859 - miss_rate: 0.3054 - fall_out: 0.0141 - mcc: 0.7433 - val_loss: 0.5854 - val_accuracy: 0.7956 - val_recall: 0.7420 - val_precision: 0.8610 - val_AUROC: 0.9800 - val_AUPRC: 0.8892 - val_f1_score: 0.7971 - val_balanced_accuracy: 0.8643 - val_specificity: 0.9867 - val_miss_rate: 0.2580 - val_fall_out: 0.0133 - val_mcc: 0.7790
Epoch 18/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6826 - accuracy: 0.7737 - recall: 0.6971 - precision: 0.8481 - AUROC: 0.9716 - AUPRC: 0.8555 - f1_score: 0.7652 - balanced_accuracy: 0.8416 - specificity: 0.9861 - miss_rate: 0.3029 - fall_out: 0.0139 - mcc: 0.7463 - val_loss: 0.5787 - val_accuracy: 0.8046 - val_recall: 0.7370 - val_precision: 0.8730 - val_AUROC: 0.9798 - val_AUPRC: 0.8905 - val_f1_score: 0.7992 - val_balanced_accuracy: 0.8625 - val_specificity: 0.9881 - val_miss_rate: 0.2630 - val_fall_out: 0.0119 - val_mcc: 0.7824
Epoch 19/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6579 - accuracy: 0.7843 - recall: 0.7124 - precision: 0.8525 - AUROC: 0.9735 - AUPRC: 0.8640 - f1_score: 0.7762 - balanced_accuracy: 0.8494 - specificity: 0.9863 - miss_rate: 0.2876 - fall_out: 0.0137 - mcc: 0.7575 - val_loss: 0.5493 - val_accuracy: 0.8161 - val_recall: 0.7565 - val_precision: 0.8653 - val_AUROC: 0.9823 - val_AUPRC: 0.8989 - val_f1_score: 0.8073 - val_balanced_accuracy: 0.8717 - val_specificity: 0.9869 - val_miss_rate: 0.2435 - val_fall_out: 0.0131 - val_mcc: 0.7896
Epoch 20/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6402 - accuracy: 0.7881 - recall: 0.7199 - precision: 0.8564 - AUROC: 0.9752 - AUPRC: 0.8692 - f1_score: 0.7823 - balanced_accuracy: 0.8533 - specificity: 0.9866 - miss_rate: 0.2801 - fall_out: 0.0134 - mcc: 0.7638 - val_loss: 0.5343 - val_accuracy: 0.8226 - val_recall: 0.7680 - val_precision: 0.8775 - val_AUROC: 0.9829 - val_AUPRC: 0.9033 - val_f1_score: 0.8191 - val_balanced_accuracy: 0.8781 - val_specificity: 0.9881 - val_miss_rate: 0.2320 - val_fall_out: 0.0119 - val_mcc: 0.8027
Epoch 21/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6285 - accuracy: 0.7993 - recall: 0.7353 - precision: 0.8588 - AUROC: 0.9753 - AUPRC: 0.8726 - f1_score: 0.7923 - balanced_accuracy: 0.8610 - specificity: 0.9866 - miss_rate: 0.2647 - fall_out: 0.0134 - mcc: 0.7740 - val_loss: 0.5225 - val_accuracy: 0.8252 - val_recall: 0.7650 - val_precision: 0.8726 - val_AUROC: 0.9840 - val_AUPRC: 0.9068 - val_f1_score: 0.8153 - val_balanced_accuracy: 0.8763 - val_specificity: 0.9876 - val_miss_rate: 0.2350 - val_fall_out: 0.0124 - val_mcc: 0.7983
Epoch 22/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6029 - accuracy: 0.8079 - recall: 0.7411 - precision: 0.8627 - AUROC: 0.9774 - AUPRC: 0.8831 - f1_score: 0.7973 - balanced_accuracy: 0.8640 - specificity: 0.9869 - miss_rate: 0.2589 - fall_out: 0.0131 - mcc: 0.7794 - val_loss: 0.5048 - val_accuracy: 0.8277 - val_recall: 0.7916 - val_precision: 0.8797 - val_AUROC: 0.9846 - val_AUPRC: 0.9128 - val_f1_score: 0.8333 - val_balanced_accuracy: 0.8898 - val_specificity: 0.9880 - val_miss_rate: 0.2084 - val_fall_out: 0.0120 - val_mcc: 0.8173
Epoch 23/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5731 - accuracy: 0.8144 - recall: 0.7600 - precision: 0.8681 - AUROC: 0.9794 - AUPRC: 0.8928 - f1_score: 0.8105 - balanced_accuracy: 0.8736 - specificity: 0.9872 - miss_rate: 0.2400 - fall_out: 0.0128 - mcc: 0.7931 - val_loss: 0.4910 - val_accuracy: 0.8282 - val_recall: 0.7821 - val_precision: 0.8849 - val_AUROC: 0.9855 - val_AUPRC: 0.9165 - val_f1_score: 0.8303 - val_balanced_accuracy: 0.8854 - val_specificity: 0.9887 - val_miss_rate: 0.2179 - val_fall_out: 0.0113 - val_mcc: 0.8146
Epoch 24/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5677 - accuracy: 0.8156 - recall: 0.7586 - precision: 0.8720 - AUROC: 0.9796 - AUPRC: 0.8935 - f1_score: 0.8114 - balanced_accuracy: 0.8731 - specificity: 0.9876 - miss_rate: 0.2414 - fall_out: 0.0124 - mcc: 0.7944 - val_loss: 0.4872 - val_accuracy: 0.8327 - val_recall: 0.7931 - val_precision: 0.8804 - val_AUROC: 0.9854 - val_AUPRC: 0.9186 - val_f1_score: 0.8345 - val_balanced_accuracy: 0.8906 - val_specificity: 0.9880 - val_miss_rate: 0.2069 - val_fall_out: 0.0120 - val_mcc: 0.8185
Epoch 25/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5430 - accuracy: 0.8244 - recall: 0.7705 - precision: 0.8757 - AUROC: 0.9813 - AUPRC: 0.9003 - f1_score: 0.8198 - balanced_accuracy: 0.8792 - specificity: 0.9879 - miss_rate: 0.2295 - fall_out: 0.0121 - mcc: 0.8032 - val_loss: 0.4870 - val_accuracy: 0.8337 - val_recall: 0.7936 - val_precision: 0.8815 - val_AUROC: 0.9854 - val_AUPRC: 0.9178 - val_f1_score: 0.8352 - val_balanced_accuracy: 0.8909 - val_specificity: 0.9881 - val_miss_rate: 0.2064 - val_fall_out: 0.0119 - val_mcc: 0.8194
Epoch 26/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5331 - accuracy: 0.8255 - recall: 0.7744 - precision: 0.8789 - AUROC: 0.9817 - AUPRC: 0.9040 - f1_score: 0.8234 - balanced_accuracy: 0.8813 - specificity: 0.9881 - miss_rate: 0.2256 - fall_out: 0.0119 - mcc: 0.8071 - val_loss: 0.4666 - val_accuracy: 0.8367 - val_recall: 0.7946 - val_precision: 0.8940 - val_AUROC: 0.9860 - val_AUPRC: 0.9213 - val_f1_score: 0.8414 - val_balanced_accuracy: 0.8921 - val_specificity: 0.9895 - val_miss_rate: 0.2054 - val_fall_out: 0.0105 - val_mcc: 0.8266
Epoch 27/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5062 - accuracy: 0.8348 - recall: 0.7836 - precision: 0.8836 - AUROC: 0.9837 - AUPRC: 0.9109 - f1_score: 0.8306 - balanced_accuracy: 0.8860 - specificity: 0.9885 - miss_rate: 0.2164 - fall_out: 0.0115 - mcc: 0.8148 - val_loss: 0.4669 - val_accuracy: 0.8382 - val_recall: 0.8041 - val_precision: 0.8790 - val_AUROC: 0.9863 - val_AUPRC: 0.9230 - val_f1_score: 0.8399 - val_balanced_accuracy: 0.8959 - val_specificity: 0.9877 - val_miss_rate: 0.1959 - val_fall_out: 0.0123 - val_mcc: 0.8240
Epoch 28/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4917 - accuracy: 0.8381 - recall: 0.7987 - precision: 0.8858 - AUROC: 0.9843 - AUPRC: 0.9157 - f1_score: 0.8400 - balanced_accuracy: 0.8936 - specificity: 0.9886 - miss_rate: 0.2013 - fall_out: 0.0114 - mcc: 0.8246 - val_loss: 0.4484 - val_accuracy: 0.8532 - val_recall: 0.8151 - val_precision: 0.8954 - val_AUROC: 0.9865 - val_AUPRC: 0.9274 - val_f1_score: 0.8534 - val_balanced_accuracy: 0.9023 - val_specificity: 0.9894 - val_miss_rate: 0.1849 - val_fall_out: 0.0106 - val_mcc: 0.8391
Epoch 29/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5018 - accuracy: 0.8444 - recall: 0.7981 - precision: 0.8871 - AUROC: 0.9834 - AUPRC: 0.9143 - f1_score: 0.8402 - balanced_accuracy: 0.8934 - specificity: 0.9887 - miss_rate: 0.2019 - fall_out: 0.0113 - mcc: 0.8249 - val_loss: 0.4482 - val_accuracy: 0.8472 - val_recall: 0.8101 - val_precision: 0.8919 - val_AUROC: 0.9874 - val_AUPRC: 0.9288 - val_f1_score: 0.8490 - val_balanced_accuracy: 0.8996 - val_specificity: 0.9891 - val_miss_rate: 0.1899 - val_fall_out: 0.0109 - val_mcc: 0.8343
Epoch 30/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4719 - accuracy: 0.8456 - recall: 0.8092 - precision: 0.8912 - AUROC: 0.9854 - AUPRC: 0.9220 - f1_score: 0.8482 - balanced_accuracy: 0.8991 - specificity: 0.9890 - miss_rate: 0.1908 - fall_out: 0.0110 - mcc: 0.8335 - val_loss: 0.4413 - val_accuracy: 0.8507 - val_recall: 0.8206 - val_precision: 0.8893 - val_AUROC: 0.9867 - val_AUPRC: 0.9297 - val_f1_score: 0.8536 - val_balanced_accuracy: 0.9046 - val_specificity: 0.9886 - val_miss_rate: 0.1794 - val_fall_out: 0.0114 - val_mcc: 0.8388
Epoch 31/100
63/63 [==============================] - 1s 11ms/step - loss: 0.4633 - accuracy: 0.8528 - recall: 0.8130 - precision: 0.8915 - AUROC: 0.9853 - AUPRC: 0.9236 - f1_score: 0.8504 - balanced_accuracy: 0.9010 - specificity: 0.9890 - miss_rate: 0.1870 - fall_out: 0.0110 - mcc: 0.8358 - val_loss: 0.4390 - val_accuracy: 0.8577 - val_recall: 0.8312 - val_precision: 0.8987 - val_AUROC: 0.9871 - val_AUPRC: 0.9309 - val_f1_score: 0.8636 - val_balanced_accuracy: 0.9104 - val_specificity: 0.9896 - val_miss_rate: 0.1688 - val_fall_out: 0.0104 - val_mcc: 0.8499
Epoch 32/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4538 - accuracy: 0.8548 - recall: 0.8185 - precision: 0.8902 - AUROC: 0.9858 - AUPRC: 0.9255 - f1_score: 0.8529 - balanced_accuracy: 0.9036 - specificity: 0.9888 - miss_rate: 0.1815 - fall_out: 0.0112 - mcc: 0.8382 - val_loss: 0.4244 - val_accuracy: 0.8597 - val_recall: 0.8292 - val_precision: 0.8931 - val_AUROC: 0.9881 - val_AUPRC: 0.9352 - val_f1_score: 0.8600 - val_balanced_accuracy: 0.9091 - val_specificity: 0.9890 - val_miss_rate: 0.1708 - val_fall_out: 0.0110 - val_mcc: 0.8458
Epoch 33/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4450 - accuracy: 0.8566 - recall: 0.8175 - precision: 0.8935 - AUROC: 0.9864 - AUPRC: 0.9283 - f1_score: 0.8538 - balanced_accuracy: 0.9033 - specificity: 0.9892 - miss_rate: 0.1825 - fall_out: 0.0108 - mcc: 0.8394 - val_loss: 0.4174 - val_accuracy: 0.8627 - val_recall: 0.8307 - val_precision: 0.8928 - val_AUROC: 0.9886 - val_AUPRC: 0.9357 - val_f1_score: 0.8606 - val_balanced_accuracy: 0.9098 - val_specificity: 0.9889 - val_miss_rate: 0.1693 - val_fall_out: 0.0111 - val_mcc: 0.8464
Epoch 34/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4395 - accuracy: 0.8639 - recall: 0.8236 - precision: 0.9013 - AUROC: 0.9858 - AUPRC: 0.9272 - f1_score: 0.8607 - balanced_accuracy: 0.9068 - specificity: 0.9900 - miss_rate: 0.1764 - fall_out: 0.0100 - mcc: 0.8471 - val_loss: 0.4270 - val_accuracy: 0.8612 - val_recall: 0.8332 - val_precision: 0.8893 - val_AUROC: 0.9878 - val_AUPRC: 0.9336 - val_f1_score: 0.8603 - val_balanced_accuracy: 0.9108 - val_specificity: 0.9885 - val_miss_rate: 0.1668 - val_fall_out: 0.0115 - val_mcc: 0.8459
Epoch 35/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4165 - accuracy: 0.8666 - recall: 0.8325 - precision: 0.9044 - AUROC: 0.9879 - AUPRC: 0.9357 - f1_score: 0.8670 - balanced_accuracy: 0.9114 - specificity: 0.9902 - miss_rate: 0.1675 - fall_out: 0.0098 - mcc: 0.8537 - val_loss: 0.4067 - val_accuracy: 0.8667 - val_recall: 0.8407 - val_precision: 0.8968 - val_AUROC: 0.9882 - val_AUPRC: 0.9379 - val_f1_score: 0.8679 - val_balanced_accuracy: 0.9150 - val_specificity: 0.9893 - val_miss_rate: 0.1593 - val_fall_out: 0.0107 - val_mcc: 0.8542
Epoch 36/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4129 - accuracy: 0.8737 - recall: 0.8397 - precision: 0.9061 - AUROC: 0.9874 - AUPRC: 0.9371 - f1_score: 0.8716 - balanced_accuracy: 0.9150 - specificity: 0.9903 - miss_rate: 0.1603 - fall_out: 0.0097 - mcc: 0.8587 - val_loss: 0.4133 - val_accuracy: 0.8627 - val_recall: 0.8387 - val_precision: 0.8909 - val_AUROC: 0.9889 - val_AUPRC: 0.9377 - val_f1_score: 0.8640 - val_balanced_accuracy: 0.9136 - val_specificity: 0.9886 - val_miss_rate: 0.1613 - val_fall_out: 0.0114 - val_mcc: 0.8499
Epoch 37/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4178 - accuracy: 0.8645 - recall: 0.8317 - precision: 0.8968 - AUROC: 0.9874 - AUPRC: 0.9334 - f1_score: 0.8630 - balanced_accuracy: 0.9105 - specificity: 0.9894 - miss_rate: 0.1683 - fall_out: 0.0106 - mcc: 0.8492 - val_loss: 0.3873 - val_accuracy: 0.8702 - val_recall: 0.8457 - val_precision: 0.8936 - val_AUROC: 0.9895 - val_AUPRC: 0.9429 - val_f1_score: 0.8690 - val_balanced_accuracy: 0.9173 - val_specificity: 0.9888 - val_miss_rate: 0.1543 - val_fall_out: 0.0112 - val_mcc: 0.8553
Epoch 38/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3959 - accuracy: 0.8735 - recall: 0.8434 - precision: 0.9058 - AUROC: 0.9892 - AUPRC: 0.9412 - f1_score: 0.8735 - balanced_accuracy: 0.9168 - specificity: 0.9903 - miss_rate: 0.1566 - fall_out: 0.0097 - mcc: 0.8607 - val_loss: 0.3944 - val_accuracy: 0.8677 - val_recall: 0.8432 - val_precision: 0.8962 - val_AUROC: 0.9893 - val_AUPRC: 0.9420 - val_f1_score: 0.8689 - val_balanced_accuracy: 0.9162 - val_specificity: 0.9891 - val_miss_rate: 0.1568 - val_fall_out: 0.0109 - val_mcc: 0.8553
Epoch 39/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3997 - accuracy: 0.8730 - recall: 0.8442 - precision: 0.9081 - AUROC: 0.9887 - AUPRC: 0.9399 - f1_score: 0.8750 - balanced_accuracy: 0.9173 - specificity: 0.9905 - miss_rate: 0.1558 - fall_out: 0.0095 - mcc: 0.8624 - val_loss: 0.4024 - val_accuracy: 0.8702 - val_recall: 0.8462 - val_precision: 0.8951 - val_AUROC: 0.9886 - val_AUPRC: 0.9404 - val_f1_score: 0.8699 - val_balanced_accuracy: 0.9176 - val_specificity: 0.9890 - val_miss_rate: 0.1538 - val_fall_out: 0.0110 - val_mcc: 0.8564
250/250 [==============================] - 1s 5ms/step - loss: 0.1509 - accuracy: 0.9573 - recall: 0.9415 - precision: 0.9694 - AUROC: 0.9988 - AUPRC: 0.9912 - f1_score: 0.9553 - balanced_accuracy: 0.9691 - specificity: 0.9967 - miss_rate: 0.0585 - fall_out: 0.0033 - mcc: 0.9505
63/63 [==============================] - 0s 5ms/step - loss: 0.4025 - accuracy: 0.8702 - recall: 0.8462 - precision: 0.8951 - AUROC: 0.9886 - AUPRC: 0.9404 - f1_score: 0.8699 - balanced_accuracy: 0.9176 - specificity: 0.9890 - miss_rate: 0.1538 - fall_out: 0.0110 - mcc: 0.8564
5it [02:41, 31.60s/it]
-- HOLDOUT 6 -- WINDOW window_3s
-- 5 Uncorrelated features: [Pearson+Spearman]
['mfcc16_mean', 'tempo', 'mfcc10_var', 'mfcc19_mean', 'mfcc17_mean']
-- Actually uncorrelated features: [confirmed via MIC]
[]
-- 1 Correlated features: [Pearson]
['spectral_centroid_mean']
Model: "MLP"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_245 (Dense) (None, 256) 14592
dropout_190 (Dropout) (None, 256) 0
dense_246 (Dense) (None, 256) 65792
dropout_191 (Dropout) (None, 256) 0
dense_247 (Dense) (None, 128) 32896
dropout_192 (Dropout) (None, 128) 0
dense_248 (Dense) (None, 128) 16512
dropout_193 (Dropout) (None, 128) 0
dense_249 (Dense) (None, 10) 1290
=================================================================
Total params: 131,082
Trainable params: 131,082
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 3s 18ms/step - loss: 2.1802 - accuracy: 0.2209 - recall: 0.0333 - precision: 0.4935 - AUROC: 0.6693 - AUPRC: 0.2059 - f1_score: 0.0624 - balanced_accuracy: 0.5148 - specificity: 0.9962 - miss_rate: 0.9667 - fall_out: 0.0038 - mcc: 0.1081 - val_loss: 1.6548 - val_accuracy: 0.3943 - val_recall: 0.1643 - val_precision: 0.7961 - val_AUROC: 0.8513 - val_AUPRC: 0.4565 - val_f1_score: 0.2724 - val_balanced_accuracy: 0.5798 - val_specificity: 0.9953 - val_miss_rate: 0.8357 - val_fall_out: 0.0047 - val_mcc: 0.3369
Epoch 2/100
63/63 [==============================] - 1s 10ms/step - loss: 1.7140 - accuracy: 0.3759 - recall: 0.1715 - precision: 0.6379 - AUROC: 0.8271 - AUPRC: 0.3950 - f1_score: 0.2703 - balanced_accuracy: 0.5803 - specificity: 0.9892 - miss_rate: 0.8285 - fall_out: 0.0108 - mcc: 0.2980 - val_loss: 1.4025 - val_accuracy: 0.4925 - val_recall: 0.2585 - val_precision: 0.7963 - val_AUROC: 0.8986 - val_AUPRC: 0.5737 - val_f1_score: 0.3903 - val_balanced_accuracy: 0.6256 - val_specificity: 0.9927 - val_miss_rate: 0.7415 - val_fall_out: 0.0073 - val_mcc: 0.4252
Epoch 3/100
63/63 [==============================] - 1s 10ms/step - loss: 1.5382 - accuracy: 0.4498 - recall: 0.2351 - precision: 0.6647 - AUROC: 0.8684 - AUPRC: 0.4774 - f1_score: 0.3473 - balanced_accuracy: 0.6110 - specificity: 0.9868 - miss_rate: 0.7649 - fall_out: 0.0132 - mcc: 0.3604 - val_loss: 1.2512 - val_accuracy: 0.5636 - val_recall: 0.3046 - val_precision: 0.7948 - val_AUROC: 0.9192 - val_AUPRC: 0.6320 - val_f1_score: 0.4404 - val_balanced_accuracy: 0.6479 - val_specificity: 0.9913 - val_miss_rate: 0.6954 - val_fall_out: 0.0087 - val_mcc: 0.4623
Epoch 4/100
63/63 [==============================] - 1s 10ms/step - loss: 1.3785 - accuracy: 0.5095 - recall: 0.3001 - precision: 0.7016 - AUROC: 0.8941 - AUPRC: 0.5557 - f1_score: 0.4204 - balanced_accuracy: 0.6430 - specificity: 0.9858 - miss_rate: 0.6999 - fall_out: 0.0142 - mcc: 0.4239 - val_loss: 1.1148 - val_accuracy: 0.6177 - val_recall: 0.3798 - val_precision: 0.8151 - val_AUROC: 0.9349 - val_AUPRC: 0.6982 - val_f1_score: 0.5181 - val_balanced_accuracy: 0.6851 - val_specificity: 0.9904 - val_miss_rate: 0.6202 - val_fall_out: 0.0096 - val_mcc: 0.5269
Epoch 5/100
63/63 [==============================] - 1s 10ms/step - loss: 1.2836 - accuracy: 0.5554 - recall: 0.3555 - precision: 0.7214 - AUROC: 0.9086 - AUPRC: 0.5962 - f1_score: 0.4763 - balanced_accuracy: 0.6701 - specificity: 0.9847 - miss_rate: 0.6445 - fall_out: 0.0153 - mcc: 0.4716 - val_loss: 1.0379 - val_accuracy: 0.6573 - val_recall: 0.4299 - val_precision: 0.8087 - val_AUROC: 0.9434 - val_AUPRC: 0.7231 - val_f1_score: 0.5613 - val_balanced_accuracy: 0.7093 - val_specificity: 0.9887 - val_miss_rate: 0.5701 - val_fall_out: 0.0113 - val_mcc: 0.5597
Epoch 6/100
63/63 [==============================] - 1s 10ms/step - loss: 1.1912 - accuracy: 0.5839 - recall: 0.3982 - precision: 0.7393 - AUROC: 0.9213 - AUPRC: 0.6403 - f1_score: 0.5176 - balanced_accuracy: 0.6913 - specificity: 0.9844 - miss_rate: 0.6018 - fall_out: 0.0156 - mcc: 0.5084 - val_loss: 0.9529 - val_accuracy: 0.6914 - val_recall: 0.4865 - val_precision: 0.8510 - val_AUROC: 0.9518 - val_AUPRC: 0.7670 - val_f1_score: 0.6191 - val_balanced_accuracy: 0.7385 - val_specificity: 0.9905 - val_miss_rate: 0.5135 - val_fall_out: 0.0095 - val_mcc: 0.6164
Epoch 7/100
63/63 [==============================] - 1s 10ms/step - loss: 1.1053 - accuracy: 0.6209 - recall: 0.4478 - precision: 0.7631 - AUROC: 0.9319 - AUPRC: 0.6842 - f1_score: 0.5644 - balanced_accuracy: 0.7162 - specificity: 0.9846 - miss_rate: 0.5522 - fall_out: 0.0154 - mcc: 0.5518 - val_loss: 0.8956 - val_accuracy: 0.6974 - val_recall: 0.5421 - val_precision: 0.8285 - val_AUROC: 0.9555 - val_AUPRC: 0.7826 - val_f1_score: 0.6554 - val_balanced_accuracy: 0.7648 - val_specificity: 0.9875 - val_miss_rate: 0.4579 - val_fall_out: 0.0125 - val_mcc: 0.6425
Epoch 8/100
63/63 [==============================] - 1s 11ms/step - loss: 1.0573 - accuracy: 0.6457 - recall: 0.4868 - precision: 0.7760 - AUROC: 0.9369 - AUPRC: 0.7062 - f1_score: 0.5983 - balanced_accuracy: 0.7356 - specificity: 0.9844 - miss_rate: 0.5132 - fall_out: 0.0156 - mcc: 0.5830 - val_loss: 0.8268 - val_accuracy: 0.7335 - val_recall: 0.5736 - val_precision: 0.8500 - val_AUROC: 0.9626 - val_AUPRC: 0.8140 - val_f1_score: 0.6850 - val_balanced_accuracy: 0.7812 - val_specificity: 0.9888 - val_miss_rate: 0.4264 - val_fall_out: 0.0112 - val_mcc: 0.6726
Epoch 9/100
63/63 [==============================] - 1s 11ms/step - loss: 0.9999 - accuracy: 0.6630 - recall: 0.5192 - precision: 0.7877 - AUROC: 0.9433 - AUPRC: 0.7311 - f1_score: 0.6258 - balanced_accuracy: 0.7518 - specificity: 0.9845 - miss_rate: 0.4808 - fall_out: 0.0155 - mcc: 0.6089 - val_loss: 0.7942 - val_accuracy: 0.7365 - val_recall: 0.6042 - val_precision: 0.8559 - val_AUROC: 0.9650 - val_AUPRC: 0.8260 - val_f1_score: 0.7084 - val_balanced_accuracy: 0.7965 - val_specificity: 0.9887 - val_miss_rate: 0.3958 - val_fall_out: 0.0113 - val_mcc: 0.6944
Epoch 10/100
63/63 [==============================] - 1s 10ms/step - loss: 0.9488 - accuracy: 0.6765 - recall: 0.5407 - precision: 0.8008 - AUROC: 0.9491 - AUPRC: 0.7538 - f1_score: 0.6455 - balanced_accuracy: 0.7629 - specificity: 0.9851 - miss_rate: 0.4593 - fall_out: 0.0149 - mcc: 0.6286 - val_loss: 0.7587 - val_accuracy: 0.7485 - val_recall: 0.6338 - val_precision: 0.8462 - val_AUROC: 0.9671 - val_AUPRC: 0.8346 - val_f1_score: 0.7247 - val_balanced_accuracy: 0.8105 - val_specificity: 0.9872 - val_miss_rate: 0.3662 - val_fall_out: 0.0128 - val_mcc: 0.7077
Epoch 11/100
63/63 [==============================] - 1s 11ms/step - loss: 0.9026 - accuracy: 0.7020 - recall: 0.5765 - precision: 0.8077 - AUROC: 0.9530 - AUPRC: 0.7750 - f1_score: 0.6728 - balanced_accuracy: 0.7806 - specificity: 0.9847 - miss_rate: 0.4235 - fall_out: 0.0153 - mcc: 0.6540 - val_loss: 0.7226 - val_accuracy: 0.7675 - val_recall: 0.6538 - val_precision: 0.8546 - val_AUROC: 0.9702 - val_AUPRC: 0.8494 - val_f1_score: 0.7408 - val_balanced_accuracy: 0.8207 - val_specificity: 0.9876 - val_miss_rate: 0.3462 - val_fall_out: 0.0124 - val_mcc: 0.7240
Epoch 12/100
63/63 [==============================] - 1s 10ms/step - loss: 0.8468 - accuracy: 0.7206 - recall: 0.6053 - precision: 0.8215 - AUROC: 0.9585 - AUPRC: 0.7972 - f1_score: 0.6971 - balanced_accuracy: 0.7954 - specificity: 0.9854 - miss_rate: 0.3947 - fall_out: 0.0146 - mcc: 0.6783 - val_loss: 0.6785 - val_accuracy: 0.7766 - val_recall: 0.6904 - val_precision: 0.8586 - val_AUROC: 0.9730 - val_AUPRC: 0.8624 - val_f1_score: 0.7653 - val_balanced_accuracy: 0.8389 - val_specificity: 0.9874 - val_miss_rate: 0.3096 - val_fall_out: 0.0126 - val_mcc: 0.7477
Epoch 13/100
63/63 [==============================] - 1s 9ms/step - loss: 0.8124 - accuracy: 0.7353 - recall: 0.6255 - precision: 0.8230 - AUROC: 0.9614 - AUPRC: 0.8084 - f1_score: 0.7108 - balanced_accuracy: 0.8053 - specificity: 0.9851 - miss_rate: 0.3745 - fall_out: 0.0149 - mcc: 0.6912 - val_loss: 0.6564 - val_accuracy: 0.7811 - val_recall: 0.7029 - val_precision: 0.8703 - val_AUROC: 0.9753 - val_AUPRC: 0.8711 - val_f1_score: 0.7777 - val_balanced_accuracy: 0.8456 - val_specificity: 0.9884 - val_miss_rate: 0.2971 - val_fall_out: 0.0116 - val_mcc: 0.7611
Epoch 14/100
63/63 [==============================] - 1s 10ms/step - loss: 0.8017 - accuracy: 0.7379 - recall: 0.6328 - precision: 0.8203 - AUROC: 0.9622 - AUPRC: 0.8100 - f1_score: 0.7144 - balanced_accuracy: 0.8087 - specificity: 0.9846 - miss_rate: 0.3672 - fall_out: 0.0154 - mcc: 0.6941 - val_loss: 0.6338 - val_accuracy: 0.7871 - val_recall: 0.7114 - val_precision: 0.8728 - val_AUROC: 0.9764 - val_AUPRC: 0.8778 - val_f1_score: 0.7839 - val_balanced_accuracy: 0.8499 - val_specificity: 0.9885 - val_miss_rate: 0.2886 - val_fall_out: 0.0115 - val_mcc: 0.7674
Epoch 15/100
63/63 [==============================] - 1s 10ms/step - loss: 0.7532 - accuracy: 0.7534 - recall: 0.6579 - precision: 0.8366 - AUROC: 0.9664 - AUPRC: 0.8298 - f1_score: 0.7366 - balanced_accuracy: 0.8218 - specificity: 0.9857 - miss_rate: 0.3421 - fall_out: 0.0143 - mcc: 0.7173 - val_loss: 0.6149 - val_accuracy: 0.7926 - val_recall: 0.7224 - val_precision: 0.8655 - val_AUROC: 0.9778 - val_AUPRC: 0.8817 - val_f1_score: 0.7875 - val_balanced_accuracy: 0.8550 - val_specificity: 0.9875 - val_miss_rate: 0.2776 - val_fall_out: 0.0125 - val_mcc: 0.7701
Epoch 16/100
63/63 [==============================] - 1s 10ms/step - loss: 0.7244 - accuracy: 0.7664 - recall: 0.6736 - precision: 0.8424 - AUROC: 0.9685 - AUPRC: 0.8398 - f1_score: 0.7486 - balanced_accuracy: 0.8298 - specificity: 0.9860 - miss_rate: 0.3264 - fall_out: 0.0140 - mcc: 0.7296 - val_loss: 0.5935 - val_accuracy: 0.7986 - val_recall: 0.7455 - val_precision: 0.8651 - val_AUROC: 0.9783 - val_AUPRC: 0.8871 - val_f1_score: 0.8009 - val_balanced_accuracy: 0.8663 - val_specificity: 0.9871 - val_miss_rate: 0.2545 - val_fall_out: 0.0129 - val_mcc: 0.7832
Epoch 17/100
63/63 [==============================] - 1s 9ms/step - loss: 0.6891 - accuracy: 0.7734 - recall: 0.6945 - precision: 0.8457 - AUROC: 0.9715 - AUPRC: 0.8509 - f1_score: 0.7627 - balanced_accuracy: 0.8402 - specificity: 0.9859 - miss_rate: 0.3055 - fall_out: 0.0141 - mcc: 0.7435 - val_loss: 0.5647 - val_accuracy: 0.8081 - val_recall: 0.7615 - val_precision: 0.8731 - val_AUROC: 0.9806 - val_AUPRC: 0.8989 - val_f1_score: 0.8135 - val_balanced_accuracy: 0.8746 - val_specificity: 0.9877 - val_miss_rate: 0.2385 - val_fall_out: 0.0123 - val_mcc: 0.7966
Epoch 18/100
63/63 [==============================] - 1s 9ms/step - loss: 0.6772 - accuracy: 0.7794 - recall: 0.7020 - precision: 0.8471 - AUROC: 0.9719 - AUPRC: 0.8553 - f1_score: 0.7678 - balanced_accuracy: 0.8440 - specificity: 0.9859 - miss_rate: 0.2980 - fall_out: 0.0141 - mcc: 0.7486 - val_loss: 0.5571 - val_accuracy: 0.8081 - val_recall: 0.7600 - val_precision: 0.8723 - val_AUROC: 0.9814 - val_AUPRC: 0.8996 - val_f1_score: 0.8123 - val_balanced_accuracy: 0.8738 - val_specificity: 0.9876 - val_miss_rate: 0.2400 - val_fall_out: 0.0124 - val_mcc: 0.7953
Epoch 19/100
63/63 [==============================] - 1s 9ms/step - loss: 0.6511 - accuracy: 0.7903 - recall: 0.7138 - precision: 0.8579 - AUROC: 0.9743 - AUPRC: 0.8677 - f1_score: 0.7792 - balanced_accuracy: 0.8503 - specificity: 0.9869 - miss_rate: 0.2862 - fall_out: 0.0131 - mcc: 0.7611 - val_loss: 0.5393 - val_accuracy: 0.8191 - val_recall: 0.7786 - val_precision: 0.8765 - val_AUROC: 0.9820 - val_AUPRC: 0.9034 - val_f1_score: 0.8246 - val_balanced_accuracy: 0.8832 - val_specificity: 0.9878 - val_miss_rate: 0.2214 - val_fall_out: 0.0122 - val_mcc: 0.8081
Epoch 20/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6281 - accuracy: 0.7962 - recall: 0.7291 - precision: 0.8594 - AUROC: 0.9754 - AUPRC: 0.8739 - f1_score: 0.7889 - balanced_accuracy: 0.8579 - specificity: 0.9868 - miss_rate: 0.2709 - fall_out: 0.0132 - mcc: 0.7707 - val_loss: 0.5081 - val_accuracy: 0.8272 - val_recall: 0.7851 - val_precision: 0.8833 - val_AUROC: 0.9834 - val_AUPRC: 0.9121 - val_f1_score: 0.8313 - val_balanced_accuracy: 0.8868 - val_specificity: 0.9885 - val_miss_rate: 0.2149 - val_fall_out: 0.0115 - val_mcc: 0.8155
Epoch 21/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6077 - accuracy: 0.8022 - recall: 0.7404 - precision: 0.8583 - AUROC: 0.9769 - AUPRC: 0.8770 - f1_score: 0.7950 - balanced_accuracy: 0.8634 - specificity: 0.9864 - miss_rate: 0.2596 - fall_out: 0.0136 - mcc: 0.7766 - val_loss: 0.5030 - val_accuracy: 0.8272 - val_recall: 0.7801 - val_precision: 0.8822 - val_AUROC: 0.9842 - val_AUPRC: 0.9137 - val_f1_score: 0.8280 - val_balanced_accuracy: 0.8842 - val_specificity: 0.9884 - val_miss_rate: 0.2199 - val_fall_out: 0.0116 - val_mcc: 0.8120
Epoch 22/100
63/63 [==============================] - 1s 11ms/step - loss: 0.5966 - accuracy: 0.8056 - recall: 0.7444 - precision: 0.8621 - AUROC: 0.9777 - AUPRC: 0.8821 - f1_score: 0.7989 - balanced_accuracy: 0.8656 - specificity: 0.9868 - miss_rate: 0.2556 - fall_out: 0.0132 - mcc: 0.7809 - val_loss: 0.4932 - val_accuracy: 0.8387 - val_recall: 0.7881 - val_precision: 0.8943 - val_AUROC: 0.9844 - val_AUPRC: 0.9179 - val_f1_score: 0.8378 - val_balanced_accuracy: 0.8889 - val_specificity: 0.9896 - val_miss_rate: 0.2119 - val_fall_out: 0.0104 - val_mcc: 0.8230
Epoch 23/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5759 - accuracy: 0.8117 - recall: 0.7533 - precision: 0.8691 - AUROC: 0.9786 - AUPRC: 0.8879 - f1_score: 0.8070 - balanced_accuracy: 0.8703 - specificity: 0.9874 - miss_rate: 0.2467 - fall_out: 0.0126 - mcc: 0.7897 - val_loss: 0.4924 - val_accuracy: 0.8357 - val_recall: 0.7866 - val_precision: 0.8865 - val_AUROC: 0.9848 - val_AUPRC: 0.9176 - val_f1_score: 0.8336 - val_balanced_accuracy: 0.8877 - val_specificity: 0.9888 - val_miss_rate: 0.2134 - val_fall_out: 0.0112 - val_mcc: 0.8181
Epoch 24/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5554 - accuracy: 0.8235 - recall: 0.7642 - precision: 0.8681 - AUROC: 0.9805 - AUPRC: 0.8953 - f1_score: 0.8128 - balanced_accuracy: 0.8756 - specificity: 0.9871 - miss_rate: 0.2358 - fall_out: 0.0129 - mcc: 0.7954 - val_loss: 0.4802 - val_accuracy: 0.8432 - val_recall: 0.8001 - val_precision: 0.8862 - val_AUROC: 0.9857 - val_AUPRC: 0.9216 - val_f1_score: 0.8410 - val_balanced_accuracy: 0.8943 - val_specificity: 0.9886 - val_miss_rate: 0.1999 - val_fall_out: 0.0114 - val_mcc: 0.8256
Epoch 25/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5494 - accuracy: 0.8151 - recall: 0.7692 - precision: 0.8688 - AUROC: 0.9805 - AUPRC: 0.8966 - f1_score: 0.8160 - balanced_accuracy: 0.8781 - specificity: 0.9871 - miss_rate: 0.2308 - fall_out: 0.0129 - mcc: 0.7987 - val_loss: 0.4710 - val_accuracy: 0.8497 - val_recall: 0.7991 - val_precision: 0.8936 - val_AUROC: 0.9859 - val_AUPRC: 0.9238 - val_f1_score: 0.8437 - val_balanced_accuracy: 0.8943 - val_specificity: 0.9894 - val_miss_rate: 0.2009 - val_fall_out: 0.0106 - val_mcc: 0.8290
Epoch 26/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5244 - accuracy: 0.8303 - recall: 0.7762 - precision: 0.8800 - AUROC: 0.9825 - AUPRC: 0.9047 - f1_score: 0.8248 - balanced_accuracy: 0.8822 - specificity: 0.9882 - miss_rate: 0.2238 - fall_out: 0.0118 - mcc: 0.8087 - val_loss: 0.4651 - val_accuracy: 0.8562 - val_recall: 0.8081 - val_precision: 0.8976 - val_AUROC: 0.9861 - val_AUPRC: 0.9238 - val_f1_score: 0.8505 - val_balanced_accuracy: 0.8989 - val_specificity: 0.9898 - val_miss_rate: 0.1919 - val_fall_out: 0.0102 - val_mcc: 0.8363
Epoch 27/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5217 - accuracy: 0.8318 - recall: 0.7823 - precision: 0.8728 - AUROC: 0.9820 - AUPRC: 0.9050 - f1_score: 0.8251 - balanced_accuracy: 0.8848 - specificity: 0.9873 - miss_rate: 0.2177 - fall_out: 0.0127 - mcc: 0.8083 - val_loss: 0.4524 - val_accuracy: 0.8507 - val_recall: 0.8136 - val_precision: 0.8962 - val_AUROC: 0.9868 - val_AUPRC: 0.9275 - val_f1_score: 0.8529 - val_balanced_accuracy: 0.9016 - val_specificity: 0.9895 - val_miss_rate: 0.1864 - val_fall_out: 0.0105 - val_mcc: 0.8387
Epoch 28/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4913 - accuracy: 0.8407 - recall: 0.7960 - precision: 0.8845 - AUROC: 0.9841 - AUPRC: 0.9135 - f1_score: 0.8379 - balanced_accuracy: 0.8922 - specificity: 0.9884 - miss_rate: 0.2040 - fall_out: 0.0116 - mcc: 0.8223 - val_loss: 0.4404 - val_accuracy: 0.8572 - val_recall: 0.8211 - val_precision: 0.8912 - val_AUROC: 0.9867 - val_AUPRC: 0.9299 - val_f1_score: 0.8548 - val_balanced_accuracy: 0.9050 - val_specificity: 0.9889 - val_miss_rate: 0.1789 - val_fall_out: 0.0111 - val_mcc: 0.8402
Epoch 29/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4942 - accuracy: 0.8401 - recall: 0.7973 - precision: 0.8822 - AUROC: 0.9842 - AUPRC: 0.9142 - f1_score: 0.8376 - balanced_accuracy: 0.8928 - specificity: 0.9882 - miss_rate: 0.2027 - fall_out: 0.0118 - mcc: 0.8219 - val_loss: 0.4275 - val_accuracy: 0.8562 - val_recall: 0.8272 - val_precision: 0.9017 - val_AUROC: 0.9884 - val_AUPRC: 0.9342 - val_f1_score: 0.8628 - val_balanced_accuracy: 0.9086 - val_specificity: 0.9900 - val_miss_rate: 0.1728 - val_fall_out: 0.0100 - val_mcc: 0.8493
Epoch 30/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4737 - accuracy: 0.8464 - recall: 0.8069 - precision: 0.8876 - AUROC: 0.9851 - AUPRC: 0.9186 - f1_score: 0.8453 - balanced_accuracy: 0.8978 - specificity: 0.9886 - miss_rate: 0.1931 - fall_out: 0.0114 - mcc: 0.8302 - val_loss: 0.4250 - val_accuracy: 0.8592 - val_recall: 0.8262 - val_precision: 0.8914 - val_AUROC: 0.9886 - val_AUPRC: 0.9351 - val_f1_score: 0.8575 - val_balanced_accuracy: 0.9075 - val_specificity: 0.9888 - val_miss_rate: 0.1738 - val_fall_out: 0.0112 - val_mcc: 0.8431
Epoch 31/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4533 - accuracy: 0.8541 - recall: 0.8143 - precision: 0.8934 - AUROC: 0.9860 - AUPRC: 0.9236 - f1_score: 0.8520 - balanced_accuracy: 0.9017 - specificity: 0.9892 - miss_rate: 0.1857 - fall_out: 0.0108 - mcc: 0.8375 - val_loss: 0.4112 - val_accuracy: 0.8592 - val_recall: 0.8292 - val_precision: 0.8990 - val_AUROC: 0.9884 - val_AUPRC: 0.9382 - val_f1_score: 0.8627 - val_balanced_accuracy: 0.9094 - val_specificity: 0.9896 - val_miss_rate: 0.1708 - val_fall_out: 0.0104 - val_mcc: 0.8489
Epoch 32/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4553 - accuracy: 0.8538 - recall: 0.8120 - precision: 0.8908 - AUROC: 0.9858 - AUPRC: 0.9249 - f1_score: 0.8496 - balanced_accuracy: 0.9005 - specificity: 0.9889 - miss_rate: 0.1880 - fall_out: 0.0111 - mcc: 0.8348 - val_loss: 0.4174 - val_accuracy: 0.8632 - val_recall: 0.8332 - val_precision: 0.8984 - val_AUROC: 0.9879 - val_AUPRC: 0.9370 - val_f1_score: 0.8646 - val_balanced_accuracy: 0.9114 - val_specificity: 0.9895 - val_miss_rate: 0.1668 - val_fall_out: 0.0105 - val_mcc: 0.8509
Epoch 33/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4468 - accuracy: 0.8572 - recall: 0.8220 - precision: 0.8943 - AUROC: 0.9863 - AUPRC: 0.9254 - f1_score: 0.8566 - balanced_accuracy: 0.9056 - specificity: 0.9892 - miss_rate: 0.1780 - fall_out: 0.0108 - mcc: 0.8423 - val_loss: 0.4122 - val_accuracy: 0.8672 - val_recall: 0.8342 - val_precision: 0.9074 - val_AUROC: 0.9884 - val_AUPRC: 0.9399 - val_f1_score: 0.8692 - val_balanced_accuracy: 0.9124 - val_specificity: 0.9905 - val_miss_rate: 0.1658 - val_fall_out: 0.0095 - val_mcc: 0.8563
250/250 [==============================] - 1s 5ms/step - loss: 0.1866 - accuracy: 0.9476 - recall: 0.9270 - precision: 0.9664 - AUROC: 0.9983 - AUPRC: 0.9878 - f1_score: 0.9463 - balanced_accuracy: 0.9617 - specificity: 0.9964 - miss_rate: 0.0730 - fall_out: 0.0036 - mcc: 0.9407
63/63 [==============================] - 0s 5ms/step - loss: 0.4122 - accuracy: 0.8672 - recall: 0.8342 - precision: 0.9074 - AUROC: 0.9884 - AUPRC: 0.9399 - f1_score: 0.8692 - balanced_accuracy: 0.9124 - specificity: 0.9905 - miss_rate: 0.1658 - fall_out: 0.0095 - mcc: 0.8563
6it [03:09, 30.42s/it]
-- HOLDOUT 7 -- WINDOW window_3s
-- 6 Uncorrelated features: [Pearson+Spearman]
['tempo', 'mfcc16_mean', 'mfcc10_var', 'mfcc19_mean', 'mfcc11_var', 'mfcc17_mean']
-- Actually uncorrelated features: [confirmed via MIC]
[]
-- 1 Correlated features: [Pearson]
['spectral_centroid_mean']
Model: "MLP"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_250 (Dense) (None, 256) 14592
dropout_194 (Dropout) (None, 256) 0
dense_251 (Dense) (None, 256) 65792
dropout_195 (Dropout) (None, 256) 0
dense_252 (Dense) (None, 128) 32896
dropout_196 (Dropout) (None, 128) 0
dense_253 (Dense) (None, 128) 16512
dropout_197 (Dropout) (None, 128) 0
dense_254 (Dense) (None, 10) 1290
=================================================================
Total params: 131,082
Trainable params: 131,082
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 2s 18ms/step - loss: 2.1701 - accuracy: 0.2256 - recall: 0.0405 - precision: 0.5465 - AUROC: 0.6677 - AUPRC: 0.2164 - f1_score: 0.0753 - balanced_accuracy: 0.5184 - specificity: 0.9963 - miss_rate: 0.9595 - fall_out: 0.0037 - mcc: 0.1285 - val_loss: 1.6538 - val_accuracy: 0.4399 - val_recall: 0.1358 - val_precision: 0.7994 - val_AUROC: 0.8525 - val_AUPRC: 0.4586 - val_f1_score: 0.2321 - val_balanced_accuracy: 0.5660 - val_specificity: 0.9962 - val_miss_rate: 0.8642 - val_fall_out: 0.0038 - val_mcc: 0.3064
Epoch 2/100
63/63 [==============================] - 1s 10ms/step - loss: 1.7388 - accuracy: 0.3819 - recall: 0.1606 - precision: 0.6423 - AUROC: 0.8243 - AUPRC: 0.3992 - f1_score: 0.2569 - balanced_accuracy: 0.5753 - specificity: 0.9901 - miss_rate: 0.8394 - fall_out: 0.0099 - mcc: 0.2895 - val_loss: 1.4006 - val_accuracy: 0.4990 - val_recall: 0.2625 - val_precision: 0.7412 - val_AUROC: 0.8986 - val_AUPRC: 0.5569 - val_f1_score: 0.3877 - val_balanced_accuracy: 0.6262 - val_specificity: 0.9898 - val_miss_rate: 0.7375 - val_fall_out: 0.0102 - val_mcc: 0.4095
Epoch 3/100
63/63 [==============================] - 1s 10ms/step - loss: 1.5340 - accuracy: 0.4533 - recall: 0.2351 - precision: 0.6888 - AUROC: 0.8671 - AUPRC: 0.4889 - f1_score: 0.3505 - balanced_accuracy: 0.6116 - specificity: 0.9882 - miss_rate: 0.7649 - fall_out: 0.0118 - mcc: 0.3689 - val_loss: 1.2042 - val_accuracy: 0.5797 - val_recall: 0.3282 - val_precision: 0.8177 - val_AUROC: 0.9285 - val_AUPRC: 0.6572 - val_f1_score: 0.4684 - val_balanced_accuracy: 0.6600 - val_specificity: 0.9919 - val_miss_rate: 0.6718 - val_fall_out: 0.0081 - val_mcc: 0.4892
Epoch 4/100
63/63 [==============================] - 1s 10ms/step - loss: 1.3799 - accuracy: 0.5111 - recall: 0.2960 - precision: 0.7148 - AUROC: 0.8937 - AUPRC: 0.5581 - f1_score: 0.4186 - balanced_accuracy: 0.6414 - specificity: 0.9869 - miss_rate: 0.7040 - fall_out: 0.0131 - mcc: 0.4259 - val_loss: 1.0814 - val_accuracy: 0.6383 - val_recall: 0.3798 - val_precision: 0.8330 - val_AUROC: 0.9417 - val_AUPRC: 0.7118 - val_f1_score: 0.5217 - val_balanced_accuracy: 0.6856 - val_specificity: 0.9915 - val_miss_rate: 0.6202 - val_fall_out: 0.0085 - val_mcc: 0.5340
Epoch 5/100
63/63 [==============================] - 1s 10ms/step - loss: 1.2682 - accuracy: 0.5601 - recall: 0.3545 - precision: 0.7436 - AUROC: 0.9105 - AUPRC: 0.6119 - f1_score: 0.4801 - balanced_accuracy: 0.6704 - specificity: 0.9864 - miss_rate: 0.6455 - fall_out: 0.0136 - mcc: 0.4800 - val_loss: 0.9789 - val_accuracy: 0.6814 - val_recall: 0.4544 - val_precision: 0.8321 - val_AUROC: 0.9490 - val_AUPRC: 0.7488 - val_f1_score: 0.5878 - val_balanced_accuracy: 0.7221 - val_specificity: 0.9898 - val_miss_rate: 0.5456 - val_fall_out: 0.0102 - val_mcc: 0.5865
Epoch 6/100
63/63 [==============================] - 1s 10ms/step - loss: 1.1615 - accuracy: 0.5994 - recall: 0.4143 - precision: 0.7424 - AUROC: 0.9247 - AUPRC: 0.6566 - f1_score: 0.5318 - balanced_accuracy: 0.6992 - specificity: 0.9840 - miss_rate: 0.5857 - fall_out: 0.0160 - mcc: 0.5206 - val_loss: 0.8873 - val_accuracy: 0.7204 - val_recall: 0.5256 - val_precision: 0.8577 - val_AUROC: 0.9584 - val_AUPRC: 0.7925 - val_f1_score: 0.6518 - val_balanced_accuracy: 0.7579 - val_specificity: 0.9903 - val_miss_rate: 0.4744 - val_fall_out: 0.0097 - val_mcc: 0.6453
Epoch 7/100
63/63 [==============================] - 1s 10ms/step - loss: 1.0932 - accuracy: 0.6308 - recall: 0.4583 - precision: 0.7625 - AUROC: 0.9332 - AUPRC: 0.6884 - f1_score: 0.5725 - balanced_accuracy: 0.7212 - specificity: 0.9841 - miss_rate: 0.5417 - fall_out: 0.0159 - mcc: 0.5584 - val_loss: 0.8546 - val_accuracy: 0.7360 - val_recall: 0.5581 - val_precision: 0.8622 - val_AUROC: 0.9615 - val_AUPRC: 0.8062 - val_f1_score: 0.6776 - val_balanced_accuracy: 0.7741 - val_specificity: 0.9901 - val_miss_rate: 0.4419 - val_fall_out: 0.0099 - val_mcc: 0.6684
Epoch 8/100
63/63 [==============================] - 1s 10ms/step - loss: 1.0186 - accuracy: 0.6642 - recall: 0.5050 - precision: 0.7875 - AUROC: 0.9411 - AUPRC: 0.7270 - f1_score: 0.6154 - balanced_accuracy: 0.7449 - specificity: 0.9849 - miss_rate: 0.4950 - fall_out: 0.0151 - mcc: 0.5999 - val_loss: 0.7913 - val_accuracy: 0.7480 - val_recall: 0.5902 - val_precision: 0.8611 - val_AUROC: 0.9664 - val_AUPRC: 0.8265 - val_f1_score: 0.7004 - val_balanced_accuracy: 0.7898 - val_specificity: 0.9894 - val_miss_rate: 0.4098 - val_fall_out: 0.0106 - val_mcc: 0.6882
Epoch 9/100
63/63 [==============================] - 1s 10ms/step - loss: 0.9759 - accuracy: 0.6747 - recall: 0.5314 - precision: 0.7915 - AUROC: 0.9458 - AUPRC: 0.7435 - f1_score: 0.6359 - balanced_accuracy: 0.7579 - specificity: 0.9844 - miss_rate: 0.4686 - fall_out: 0.0156 - mcc: 0.6184 - val_loss: 0.7633 - val_accuracy: 0.7540 - val_recall: 0.6107 - val_precision: 0.8658 - val_AUROC: 0.9685 - val_AUPRC: 0.8354 - val_f1_score: 0.7162 - val_balanced_accuracy: 0.8001 - val_specificity: 0.9895 - val_miss_rate: 0.3893 - val_fall_out: 0.0105 - val_mcc: 0.7032
Epoch 10/100
63/63 [==============================] - 1s 10ms/step - loss: 0.9294 - accuracy: 0.6965 - recall: 0.5580 - precision: 0.8052 - AUROC: 0.9502 - AUPRC: 0.7619 - f1_score: 0.6592 - balanced_accuracy: 0.7715 - specificity: 0.9850 - miss_rate: 0.4420 - fall_out: 0.0150 - mcc: 0.6414 - val_loss: 0.7088 - val_accuracy: 0.7705 - val_recall: 0.6643 - val_precision: 0.8695 - val_AUROC: 0.9719 - val_AUPRC: 0.8532 - val_f1_score: 0.7532 - val_balanced_accuracy: 0.8266 - val_specificity: 0.9889 - val_miss_rate: 0.3357 - val_fall_out: 0.0111 - val_mcc: 0.7377
Epoch 11/100
63/63 [==============================] - 1s 10ms/step - loss: 0.8811 - accuracy: 0.7098 - recall: 0.5938 - precision: 0.8095 - AUROC: 0.9547 - AUPRC: 0.7803 - f1_score: 0.6851 - balanced_accuracy: 0.7891 - specificity: 0.9845 - miss_rate: 0.4062 - fall_out: 0.0155 - mcc: 0.6654 - val_loss: 0.6794 - val_accuracy: 0.7826 - val_recall: 0.6698 - val_precision: 0.8716 - val_AUROC: 0.9741 - val_AUPRC: 0.8615 - val_f1_score: 0.7575 - val_balanced_accuracy: 0.8294 - val_specificity: 0.9890 - val_miss_rate: 0.3302 - val_fall_out: 0.0110 - val_mcc: 0.7421
Epoch 12/100
63/63 [==============================] - 1s 10ms/step - loss: 0.8545 - accuracy: 0.7184 - recall: 0.6052 - precision: 0.8179 - AUROC: 0.9574 - AUPRC: 0.7922 - f1_score: 0.6957 - balanced_accuracy: 0.7951 - specificity: 0.9850 - miss_rate: 0.3948 - fall_out: 0.0150 - mcc: 0.6764 - val_loss: 0.6549 - val_accuracy: 0.7851 - val_recall: 0.6929 - val_precision: 0.8687 - val_AUROC: 0.9749 - val_AUPRC: 0.8696 - val_f1_score: 0.7709 - val_balanced_accuracy: 0.8406 - val_specificity: 0.9884 - val_miss_rate: 0.3071 - val_fall_out: 0.0116 - val_mcc: 0.7544
Epoch 13/100
63/63 [==============================] - 1s 10ms/step - loss: 0.8104 - accuracy: 0.7377 - recall: 0.6351 - precision: 0.8215 - AUROC: 0.9611 - AUPRC: 0.8097 - f1_score: 0.7164 - balanced_accuracy: 0.8099 - specificity: 0.9847 - miss_rate: 0.3649 - fall_out: 0.0153 - mcc: 0.6962 - val_loss: 0.6223 - val_accuracy: 0.8021 - val_recall: 0.7084 - val_precision: 0.8871 - val_AUROC: 0.9782 - val_AUPRC: 0.8819 - val_f1_score: 0.7877 - val_balanced_accuracy: 0.8492 - val_specificity: 0.9900 - val_miss_rate: 0.2916 - val_fall_out: 0.0100 - val_mcc: 0.7729
Epoch 14/100
63/63 [==============================] - 1s 10ms/step - loss: 0.7821 - accuracy: 0.7465 - recall: 0.6444 - precision: 0.8335 - AUROC: 0.9638 - AUPRC: 0.8222 - f1_score: 0.7268 - balanced_accuracy: 0.8151 - specificity: 0.9857 - miss_rate: 0.3556 - fall_out: 0.0143 - mcc: 0.7077 - val_loss: 0.6132 - val_accuracy: 0.8001 - val_recall: 0.7249 - val_precision: 0.8722 - val_AUROC: 0.9775 - val_AUPRC: 0.8806 - val_f1_score: 0.7918 - val_balanced_accuracy: 0.8566 - val_specificity: 0.9882 - val_miss_rate: 0.2751 - val_fall_out: 0.0118 - val_mcc: 0.7750
Epoch 15/100
63/63 [==============================] - 1s 10ms/step - loss: 0.7302 - accuracy: 0.7559 - recall: 0.6687 - precision: 0.8355 - AUROC: 0.9683 - AUPRC: 0.8403 - f1_score: 0.7429 - balanced_accuracy: 0.8270 - specificity: 0.9854 - miss_rate: 0.3313 - fall_out: 0.0146 - mcc: 0.7232 - val_loss: 0.5956 - val_accuracy: 0.8026 - val_recall: 0.7340 - val_precision: 0.8762 - val_AUROC: 0.9789 - val_AUPRC: 0.8867 - val_f1_score: 0.7988 - val_balanced_accuracy: 0.8612 - val_specificity: 0.9885 - val_miss_rate: 0.2660 - val_fall_out: 0.0115 - val_mcc: 0.7823
Epoch 16/100
63/63 [==============================] - 1s 10ms/step - loss: 0.7109 - accuracy: 0.7652 - recall: 0.6807 - precision: 0.8433 - AUROC: 0.9693 - AUPRC: 0.8451 - f1_score: 0.7533 - balanced_accuracy: 0.8333 - specificity: 0.9859 - miss_rate: 0.3193 - fall_out: 0.0141 - mcc: 0.7342 - val_loss: 0.5708 - val_accuracy: 0.8121 - val_recall: 0.7390 - val_precision: 0.8728 - val_AUROC: 0.9806 - val_AUPRC: 0.8940 - val_f1_score: 0.8003 - val_balanced_accuracy: 0.8635 - val_specificity: 0.9880 - val_miss_rate: 0.2610 - val_fall_out: 0.0120 - val_mcc: 0.7834
Epoch 17/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6825 - accuracy: 0.7714 - recall: 0.6973 - precision: 0.8482 - AUROC: 0.9721 - AUPRC: 0.8555 - f1_score: 0.7654 - balanced_accuracy: 0.8417 - specificity: 0.9861 - miss_rate: 0.3027 - fall_out: 0.0139 - mcc: 0.7464 - val_loss: 0.5470 - val_accuracy: 0.8282 - val_recall: 0.7470 - val_precision: 0.8875 - val_AUROC: 0.9824 - val_AUPRC: 0.9028 - val_f1_score: 0.8112 - val_balanced_accuracy: 0.8682 - val_specificity: 0.9895 - val_miss_rate: 0.2530 - val_fall_out: 0.0105 - val_mcc: 0.7958
Epoch 18/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6726 - accuracy: 0.7767 - recall: 0.7073 - precision: 0.8492 - AUROC: 0.9724 - AUPRC: 0.8590 - f1_score: 0.7718 - balanced_accuracy: 0.8467 - specificity: 0.9860 - miss_rate: 0.2927 - fall_out: 0.0140 - mcc: 0.7527 - val_loss: 0.5382 - val_accuracy: 0.8342 - val_recall: 0.7670 - val_precision: 0.8886 - val_AUROC: 0.9819 - val_AUPRC: 0.9033 - val_f1_score: 0.8233 - val_balanced_accuracy: 0.8782 - val_specificity: 0.9893 - val_miss_rate: 0.2330 - val_fall_out: 0.0107 - val_mcc: 0.8079
Epoch 19/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6360 - accuracy: 0.7963 - recall: 0.7252 - precision: 0.8593 - AUROC: 0.9749 - AUPRC: 0.8705 - f1_score: 0.7866 - balanced_accuracy: 0.8560 - specificity: 0.9868 - miss_rate: 0.2748 - fall_out: 0.0132 - mcc: 0.7684 - val_loss: 0.5302 - val_accuracy: 0.8267 - val_recall: 0.7690 - val_precision: 0.8832 - val_AUROC: 0.9827 - val_AUPRC: 0.9052 - val_f1_score: 0.8222 - val_balanced_accuracy: 0.8789 - val_specificity: 0.9887 - val_miss_rate: 0.2310 - val_fall_out: 0.0113 - val_mcc: 0.8063
Epoch 20/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6352 - accuracy: 0.7955 - recall: 0.7207 - precision: 0.8535 - AUROC: 0.9755 - AUPRC: 0.8720 - f1_score: 0.7815 - balanced_accuracy: 0.8535 - specificity: 0.9863 - miss_rate: 0.2793 - fall_out: 0.0137 - mcc: 0.7627 - val_loss: 0.5126 - val_accuracy: 0.8322 - val_recall: 0.7781 - val_precision: 0.8859 - val_AUROC: 0.9824 - val_AUPRC: 0.9084 - val_f1_score: 0.8285 - val_balanced_accuracy: 0.8835 - val_specificity: 0.9889 - val_miss_rate: 0.2219 - val_fall_out: 0.0111 - val_mcc: 0.8129
Epoch 21/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6048 - accuracy: 0.8011 - recall: 0.7391 - precision: 0.8613 - AUROC: 0.9772 - AUPRC: 0.8810 - f1_score: 0.7956 - balanced_accuracy: 0.8629 - specificity: 0.9868 - miss_rate: 0.2609 - fall_out: 0.0132 - mcc: 0.7775 - val_loss: 0.4932 - val_accuracy: 0.8482 - val_recall: 0.7926 - val_precision: 0.8898 - val_AUROC: 0.9834 - val_AUPRC: 0.9143 - val_f1_score: 0.8384 - val_balanced_accuracy: 0.8908 - val_specificity: 0.9891 - val_miss_rate: 0.2074 - val_fall_out: 0.0109 - val_mcc: 0.8232
Epoch 22/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5781 - accuracy: 0.8092 - recall: 0.7501 - precision: 0.8666 - AUROC: 0.9790 - AUPRC: 0.8902 - f1_score: 0.8042 - balanced_accuracy: 0.8686 - specificity: 0.9872 - miss_rate: 0.2499 - fall_out: 0.0128 - mcc: 0.7866 - val_loss: 0.4811 - val_accuracy: 0.8427 - val_recall: 0.7881 - val_precision: 0.8963 - val_AUROC: 0.9857 - val_AUPRC: 0.9199 - val_f1_score: 0.8387 - val_balanced_accuracy: 0.8890 - val_specificity: 0.9899 - val_miss_rate: 0.2119 - val_fall_out: 0.0101 - val_mcc: 0.8241
Epoch 23/100
63/63 [==============================] - 1s 11ms/step - loss: 0.5675 - accuracy: 0.8189 - recall: 0.7620 - precision: 0.8706 - AUROC: 0.9793 - AUPRC: 0.8930 - f1_score: 0.8127 - balanced_accuracy: 0.8747 - specificity: 0.9874 - miss_rate: 0.2380 - fall_out: 0.0126 - mcc: 0.7956 - val_loss: 0.4685 - val_accuracy: 0.8512 - val_recall: 0.8091 - val_precision: 0.8928 - val_AUROC: 0.9845 - val_AUPRC: 0.9192 - val_f1_score: 0.8489 - val_balanced_accuracy: 0.8992 - val_specificity: 0.9892 - val_miss_rate: 0.1909 - val_fall_out: 0.0108 - val_mcc: 0.8342
Epoch 24/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5499 - accuracy: 0.8234 - recall: 0.7690 - precision: 0.8750 - AUROC: 0.9808 - AUPRC: 0.8997 - f1_score: 0.8186 - balanced_accuracy: 0.8784 - specificity: 0.9878 - miss_rate: 0.2310 - fall_out: 0.0122 - mcc: 0.8019 - val_loss: 0.4537 - val_accuracy: 0.8582 - val_recall: 0.8086 - val_precision: 0.8972 - val_AUROC: 0.9865 - val_AUPRC: 0.9249 - val_f1_score: 0.8506 - val_balanced_accuracy: 0.8992 - val_specificity: 0.9897 - val_miss_rate: 0.1914 - val_fall_out: 0.0103 - val_mcc: 0.8363
Epoch 25/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5224 - accuracy: 0.8333 - recall: 0.7841 - precision: 0.8816 - AUROC: 0.9825 - AUPRC: 0.9072 - f1_score: 0.8300 - balanced_accuracy: 0.8862 - specificity: 0.9883 - miss_rate: 0.2159 - fall_out: 0.0117 - mcc: 0.8140 - val_loss: 0.4427 - val_accuracy: 0.8597 - val_recall: 0.8166 - val_precision: 0.9020 - val_AUROC: 0.9865 - val_AUPRC: 0.9277 - val_f1_score: 0.8572 - val_balanced_accuracy: 0.9034 - val_specificity: 0.9901 - val_miss_rate: 0.1834 - val_fall_out: 0.0099 - val_mcc: 0.8435
Epoch 26/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5076 - accuracy: 0.8392 - recall: 0.7920 - precision: 0.8845 - AUROC: 0.9835 - AUPRC: 0.9111 - f1_score: 0.8357 - balanced_accuracy: 0.8902 - specificity: 0.9885 - miss_rate: 0.2080 - fall_out: 0.0115 - mcc: 0.8200 - val_loss: 0.4407 - val_accuracy: 0.8667 - val_recall: 0.8297 - val_precision: 0.9015 - val_AUROC: 0.9853 - val_AUPRC: 0.9260 - val_f1_score: 0.8641 - val_balanced_accuracy: 0.9098 - val_specificity: 0.9899 - val_miss_rate: 0.1703 - val_fall_out: 0.0101 - val_mcc: 0.8506
Epoch 27/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5053 - accuracy: 0.8379 - recall: 0.7893 - precision: 0.8826 - AUROC: 0.9837 - AUPRC: 0.9124 - f1_score: 0.8334 - balanced_accuracy: 0.8888 - specificity: 0.9883 - miss_rate: 0.2107 - fall_out: 0.0117 - mcc: 0.8176 - val_loss: 0.4383 - val_accuracy: 0.8672 - val_recall: 0.8297 - val_precision: 0.9039 - val_AUROC: 0.9868 - val_AUPRC: 0.9283 - val_f1_score: 0.8652 - val_balanced_accuracy: 0.9099 - val_specificity: 0.9902 - val_miss_rate: 0.1703 - val_fall_out: 0.0098 - val_mcc: 0.8519
Epoch 28/100
63/63 [==============================] - 1s 11ms/step - loss: 0.4935 - accuracy: 0.8438 - recall: 0.7970 - precision: 0.8888 - AUROC: 0.9838 - AUPRC: 0.9160 - f1_score: 0.8404 - balanced_accuracy: 0.8929 - specificity: 0.9889 - miss_rate: 0.2030 - fall_out: 0.0111 - mcc: 0.8252 - val_loss: 0.4251 - val_accuracy: 0.8632 - val_recall: 0.8302 - val_precision: 0.9055 - val_AUROC: 0.9870 - val_AUPRC: 0.9330 - val_f1_score: 0.8662 - val_balanced_accuracy: 0.9103 - val_specificity: 0.9904 - val_miss_rate: 0.1698 - val_fall_out: 0.0096 - val_mcc: 0.8530
Epoch 29/100
63/63 [==============================] - 1s 11ms/step - loss: 0.4713 - accuracy: 0.8448 - recall: 0.8040 - precision: 0.8900 - AUROC: 0.9856 - AUPRC: 0.9220 - f1_score: 0.8448 - balanced_accuracy: 0.8965 - specificity: 0.9890 - miss_rate: 0.1960 - fall_out: 0.0110 - mcc: 0.8299 - val_loss: 0.4259 - val_accuracy: 0.8647 - val_recall: 0.8352 - val_precision: 0.9040 - val_AUROC: 0.9865 - val_AUPRC: 0.9298 - val_f1_score: 0.8682 - val_balanced_accuracy: 0.9127 - val_specificity: 0.9901 - val_miss_rate: 0.1648 - val_fall_out: 0.0099 - val_mcc: 0.8550
Epoch 30/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4566 - accuracy: 0.8493 - recall: 0.8097 - precision: 0.8930 - AUROC: 0.9858 - AUPRC: 0.9239 - f1_score: 0.8493 - balanced_accuracy: 0.8995 - specificity: 0.9892 - miss_rate: 0.1903 - fall_out: 0.0108 - mcc: 0.8347 - val_loss: 0.4114 - val_accuracy: 0.8627 - val_recall: 0.8392 - val_precision: 0.8952 - val_AUROC: 0.9877 - val_AUPRC: 0.9351 - val_f1_score: 0.8663 - val_balanced_accuracy: 0.9141 - val_specificity: 0.9891 - val_miss_rate: 0.1608 - val_fall_out: 0.0109 - val_mcc: 0.8525
Epoch 31/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4514 - accuracy: 0.8528 - recall: 0.8165 - precision: 0.8913 - AUROC: 0.9864 - AUPRC: 0.9257 - f1_score: 0.8523 - balanced_accuracy: 0.9027 - specificity: 0.9889 - miss_rate: 0.1835 - fall_out: 0.0111 - mcc: 0.8376 - val_loss: 0.4089 - val_accuracy: 0.8702 - val_recall: 0.8412 - val_precision: 0.9032 - val_AUROC: 0.9877 - val_AUPRC: 0.9364 - val_f1_score: 0.8711 - val_balanced_accuracy: 0.9156 - val_specificity: 0.9900 - val_miss_rate: 0.1588 - val_fall_out: 0.0100 - val_mcc: 0.8580
Epoch 32/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4395 - accuracy: 0.8577 - recall: 0.8206 - precision: 0.8966 - AUROC: 0.9866 - AUPRC: 0.9281 - f1_score: 0.8569 - balanced_accuracy: 0.9051 - specificity: 0.9895 - miss_rate: 0.1794 - fall_out: 0.0105 - mcc: 0.8428 - val_loss: 0.4137 - val_accuracy: 0.8642 - val_recall: 0.8432 - val_precision: 0.8981 - val_AUROC: 0.9880 - val_AUPRC: 0.9366 - val_f1_score: 0.8698 - val_balanced_accuracy: 0.9163 - val_specificity: 0.9894 - val_miss_rate: 0.1568 - val_fall_out: 0.0106 - val_mcc: 0.8563
Epoch 33/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4189 - accuracy: 0.8672 - recall: 0.8344 - precision: 0.9023 - AUROC: 0.9876 - AUPRC: 0.9348 - f1_score: 0.8671 - balanced_accuracy: 0.9122 - specificity: 0.9900 - miss_rate: 0.1656 - fall_out: 0.0100 - mcc: 0.8537 - val_loss: 0.4026 - val_accuracy: 0.8753 - val_recall: 0.8477 - val_precision: 0.9068 - val_AUROC: 0.9879 - val_AUPRC: 0.9389 - val_f1_score: 0.8762 - val_balanced_accuracy: 0.9190 - val_specificity: 0.9903 - val_miss_rate: 0.1523 - val_fall_out: 0.0097 - val_mcc: 0.8636
Epoch 34/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4268 - accuracy: 0.8630 - recall: 0.8282 - precision: 0.8965 - AUROC: 0.9874 - AUPRC: 0.9313 - f1_score: 0.8610 - balanced_accuracy: 0.9088 - specificity: 0.9894 - miss_rate: 0.1718 - fall_out: 0.0106 - mcc: 0.8470 - val_loss: 0.4051 - val_accuracy: 0.8712 - val_recall: 0.8422 - val_precision: 0.8999 - val_AUROC: 0.9880 - val_AUPRC: 0.9376 - val_f1_score: 0.8701 - val_balanced_accuracy: 0.9159 - val_specificity: 0.9896 - val_miss_rate: 0.1578 - val_fall_out: 0.0104 - val_mcc: 0.8568
Epoch 35/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4126 - accuracy: 0.8679 - recall: 0.8323 - precision: 0.9027 - AUROC: 0.9884 - AUPRC: 0.9368 - f1_score: 0.8661 - balanced_accuracy: 0.9112 - specificity: 0.9900 - miss_rate: 0.1677 - fall_out: 0.0100 - mcc: 0.8527 - val_loss: 0.4025 - val_accuracy: 0.8712 - val_recall: 0.8447 - val_precision: 0.9045 - val_AUROC: 0.9869 - val_AUPRC: 0.9385 - val_f1_score: 0.8736 - val_balanced_accuracy: 0.9174 - val_specificity: 0.9901 - val_miss_rate: 0.1553 - val_fall_out: 0.0099 - val_mcc: 0.8607
Epoch 36/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4022 - accuracy: 0.8674 - recall: 0.8319 - precision: 0.9009 - AUROC: 0.9889 - AUPRC: 0.9397 - f1_score: 0.8650 - balanced_accuracy: 0.9109 - specificity: 0.9898 - miss_rate: 0.1681 - fall_out: 0.0102 - mcc: 0.8515 - val_loss: 0.3959 - val_accuracy: 0.8742 - val_recall: 0.8572 - val_precision: 0.9053 - val_AUROC: 0.9872 - val_AUPRC: 0.9401 - val_f1_score: 0.8806 - val_balanced_accuracy: 0.9236 - val_specificity: 0.9900 - val_miss_rate: 0.1428 - val_fall_out: 0.0100 - val_mcc: 0.8681
Epoch 37/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3808 - accuracy: 0.8769 - recall: 0.8449 - precision: 0.9060 - AUROC: 0.9898 - AUPRC: 0.9432 - f1_score: 0.8744 - balanced_accuracy: 0.9176 - specificity: 0.9903 - miss_rate: 0.1551 - fall_out: 0.0097 - mcc: 0.8616 - val_loss: 0.3838 - val_accuracy: 0.8813 - val_recall: 0.8627 - val_precision: 0.9087 - val_AUROC: 0.9882 - val_AUPRC: 0.9420 - val_f1_score: 0.8851 - val_balanced_accuracy: 0.9265 - val_specificity: 0.9904 - val_miss_rate: 0.1373 - val_fall_out: 0.0096 - val_mcc: 0.8731
Epoch 38/100
63/63 [==============================] - 1s 9ms/step - loss: 0.3980 - accuracy: 0.8736 - recall: 0.8408 - precision: 0.9051 - AUROC: 0.9886 - AUPRC: 0.9388 - f1_score: 0.8718 - balanced_accuracy: 0.9155 - specificity: 0.9902 - miss_rate: 0.1592 - fall_out: 0.0098 - mcc: 0.8588 - val_loss: 0.3946 - val_accuracy: 0.8788 - val_recall: 0.8547 - val_precision: 0.9041 - val_AUROC: 0.9872 - val_AUPRC: 0.9384 - val_f1_score: 0.8787 - val_balanced_accuracy: 0.9223 - val_specificity: 0.9899 - val_miss_rate: 0.1453 - val_fall_out: 0.0101 - val_mcc: 0.8661
Epoch 39/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3717 - accuracy: 0.8779 - recall: 0.8532 - precision: 0.9081 - AUROC: 0.9904 - AUPRC: 0.9462 - f1_score: 0.8798 - balanced_accuracy: 0.9218 - specificity: 0.9904 - miss_rate: 0.1468 - fall_out: 0.0096 - mcc: 0.8674 - val_loss: 0.4051 - val_accuracy: 0.8768 - val_recall: 0.8542 - val_precision: 0.9002 - val_AUROC: 0.9873 - val_AUPRC: 0.9372 - val_f1_score: 0.8766 - val_balanced_accuracy: 0.9218 - val_specificity: 0.9895 - val_miss_rate: 0.1458 - val_fall_out: 0.0105 - val_mcc: 0.8637
250/250 [==============================] - 1s 5ms/step - loss: 0.1329 - accuracy: 0.9609 - recall: 0.9465 - precision: 0.9737 - AUROC: 0.9991 - AUPRC: 0.9935 - f1_score: 0.9599 - balanced_accuracy: 0.9718 - specificity: 0.9972 - miss_rate: 0.0535 - fall_out: 0.0028 - mcc: 0.9557
63/63 [==============================] - 0s 5ms/step - loss: 0.4051 - accuracy: 0.8768 - recall: 0.8542 - precision: 0.9002 - AUROC: 0.9873 - AUPRC: 0.9372 - f1_score: 0.8766 - balanced_accuracy: 0.9218 - specificity: 0.9895 - miss_rate: 0.1458 - fall_out: 0.0105 - mcc: 0.8637
7it [03:42, 31.11s/it]
-- HOLDOUT 8 -- WINDOW window_3s
-- 6 Uncorrelated features: [Pearson+Spearman]
['mfcc16_mean', 'tempo', 'mfcc10_var', 'mfcc19_mean', 'mfcc11_var', 'mfcc17_mean']
-- Actually uncorrelated features: [confirmed via MIC]
[]
-- 1 Correlated features: [Pearson]
['spectral_centroid_mean']
Model: "MLP"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_255 (Dense) (None, 256) 14592
dropout_198 (Dropout) (None, 256) 0
dense_256 (Dense) (None, 256) 65792
dropout_199 (Dropout) (None, 256) 0
dense_257 (Dense) (None, 128) 32896
dropout_200 (Dropout) (None, 128) 0
dense_258 (Dense) (None, 128) 16512
dropout_201 (Dropout) (None, 128) 0
dense_259 (Dense) (None, 10) 1290
=================================================================
Total params: 131,082
Trainable params: 131,082
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 2s 19ms/step - loss: 2.2044 - accuracy: 0.2194 - recall: 0.0316 - precision: 0.4865 - AUROC: 0.6586 - AUPRC: 0.2008 - f1_score: 0.0593 - balanced_accuracy: 0.5139 - specificity: 0.9963 - miss_rate: 0.9684 - fall_out: 0.0037 - mcc: 0.1041 - val_loss: 1.6817 - val_accuracy: 0.3692 - val_recall: 0.1683 - val_precision: 0.7304 - val_AUROC: 0.8465 - val_AUPRC: 0.4389 - val_f1_score: 0.2736 - val_balanced_accuracy: 0.5807 - val_specificity: 0.9931 - val_miss_rate: 0.8317 - val_fall_out: 0.0069 - val_mcc: 0.3228
Epoch 2/100
63/63 [==============================] - 1s 9ms/step - loss: 1.7602 - accuracy: 0.3659 - recall: 0.1541 - precision: 0.6390 - AUROC: 0.8161 - AUPRC: 0.3820 - f1_score: 0.2483 - balanced_accuracy: 0.5722 - specificity: 0.9903 - miss_rate: 0.8459 - fall_out: 0.0097 - mcc: 0.2824 - val_loss: 1.3950 - val_accuracy: 0.4940 - val_recall: 0.2605 - val_precision: 0.7569 - val_AUROC: 0.9001 - val_AUPRC: 0.5650 - val_f1_score: 0.3876 - val_balanced_accuracy: 0.6256 - val_specificity: 0.9907 - val_miss_rate: 0.7395 - val_fall_out: 0.0093 - val_mcc: 0.4134
Epoch 3/100
63/63 [==============================] - 1s 9ms/step - loss: 1.5552 - accuracy: 0.4426 - recall: 0.2236 - precision: 0.6713 - AUROC: 0.8631 - AUPRC: 0.4724 - f1_score: 0.3354 - balanced_accuracy: 0.6057 - specificity: 0.9878 - miss_rate: 0.7764 - fall_out: 0.0122 - mcc: 0.3535 - val_loss: 1.2246 - val_accuracy: 0.5696 - val_recall: 0.3136 - val_precision: 0.7757 - val_AUROC: 0.9249 - val_AUPRC: 0.6347 - val_f1_score: 0.4467 - val_balanced_accuracy: 0.6518 - val_specificity: 0.9899 - val_miss_rate: 0.6864 - val_fall_out: 0.0101 - val_mcc: 0.4623
Epoch 4/100
63/63 [==============================] - 1s 10ms/step - loss: 1.4171 - accuracy: 0.5050 - recall: 0.2844 - precision: 0.6973 - AUROC: 0.8893 - AUPRC: 0.5378 - f1_score: 0.4041 - balanced_accuracy: 0.6354 - specificity: 0.9863 - miss_rate: 0.7156 - fall_out: 0.0137 - mcc: 0.4106 - val_loss: 1.0933 - val_accuracy: 0.6288 - val_recall: 0.3662 - val_precision: 0.8186 - val_AUROC: 0.9417 - val_AUPRC: 0.7034 - val_f1_score: 0.5061 - val_balanced_accuracy: 0.6786 - val_specificity: 0.9910 - val_miss_rate: 0.6338 - val_fall_out: 0.0090 - val_mcc: 0.5184
Epoch 5/100
63/63 [==============================] - 1s 10ms/step - loss: 1.2752 - accuracy: 0.5551 - recall: 0.3523 - precision: 0.7286 - AUROC: 0.9102 - AUPRC: 0.6060 - f1_score: 0.4750 - balanced_accuracy: 0.6689 - specificity: 0.9854 - miss_rate: 0.6477 - fall_out: 0.0146 - mcc: 0.4723 - val_loss: 0.9899 - val_accuracy: 0.6748 - val_recall: 0.4429 - val_precision: 0.8451 - val_AUROC: 0.9509 - val_AUPRC: 0.7469 - val_f1_score: 0.5812 - val_balanced_accuracy: 0.7169 - val_specificity: 0.9910 - val_miss_rate: 0.5571 - val_fall_out: 0.0090 - val_mcc: 0.5841
Epoch 6/100
63/63 [==============================] - 1s 10ms/step - loss: 1.1934 - accuracy: 0.5901 - recall: 0.3964 - precision: 0.7444 - AUROC: 0.9213 - AUPRC: 0.6434 - f1_score: 0.5173 - balanced_accuracy: 0.6906 - specificity: 0.9849 - miss_rate: 0.6036 - fall_out: 0.0151 - mcc: 0.5094 - val_loss: 0.9035 - val_accuracy: 0.6924 - val_recall: 0.4975 - val_precision: 0.8437 - val_AUROC: 0.9582 - val_AUPRC: 0.7792 - val_f1_score: 0.6259 - val_balanced_accuracy: 0.7436 - val_specificity: 0.9898 - val_miss_rate: 0.5025 - val_fall_out: 0.0102 - val_mcc: 0.6205
Epoch 7/100
63/63 [==============================] - 1s 10ms/step - loss: 1.1095 - accuracy: 0.6196 - recall: 0.4540 - precision: 0.7640 - AUROC: 0.9308 - AUPRC: 0.6823 - f1_score: 0.5696 - balanced_accuracy: 0.7192 - specificity: 0.9844 - miss_rate: 0.5460 - fall_out: 0.0156 - mcc: 0.5563 - val_loss: 0.8415 - val_accuracy: 0.7204 - val_recall: 0.5406 - val_precision: 0.8618 - val_AUROC: 0.9638 - val_AUPRC: 0.8040 - val_f1_score: 0.6644 - val_balanced_accuracy: 0.7655 - val_specificity: 0.9904 - val_miss_rate: 0.4594 - val_fall_out: 0.0096 - val_mcc: 0.6569
Epoch 8/100
63/63 [==============================] - 1s 10ms/step - loss: 1.0625 - accuracy: 0.6464 - recall: 0.4768 - precision: 0.7735 - AUROC: 0.9362 - AUPRC: 0.7032 - f1_score: 0.5900 - balanced_accuracy: 0.7307 - specificity: 0.9845 - miss_rate: 0.5232 - fall_out: 0.0155 - mcc: 0.5754 - val_loss: 0.8006 - val_accuracy: 0.7214 - val_recall: 0.5777 - val_precision: 0.8441 - val_AUROC: 0.9667 - val_AUPRC: 0.8167 - val_f1_score: 0.6859 - val_balanced_accuracy: 0.7829 - val_specificity: 0.9881 - val_miss_rate: 0.4223 - val_fall_out: 0.0119 - val_mcc: 0.6723
Epoch 9/100
63/63 [==============================] - 1s 10ms/step - loss: 0.9824 - accuracy: 0.6687 - recall: 0.5212 - precision: 0.7872 - AUROC: 0.9454 - AUPRC: 0.7380 - f1_score: 0.6271 - balanced_accuracy: 0.7528 - specificity: 0.9843 - miss_rate: 0.4788 - fall_out: 0.0157 - mcc: 0.6099 - val_loss: 0.7775 - val_accuracy: 0.7260 - val_recall: 0.5952 - val_precision: 0.8273 - val_AUROC: 0.9674 - val_AUPRC: 0.8185 - val_f1_score: 0.6923 - val_balanced_accuracy: 0.7907 - val_specificity: 0.9862 - val_miss_rate: 0.4048 - val_fall_out: 0.0138 - val_mcc: 0.6750
Epoch 10/100
63/63 [==============================] - 1s 10ms/step - loss: 0.9423 - accuracy: 0.6856 - recall: 0.5496 - precision: 0.7958 - AUROC: 0.9489 - AUPRC: 0.7552 - f1_score: 0.6502 - balanced_accuracy: 0.7670 - specificity: 0.9843 - miss_rate: 0.4504 - fall_out: 0.0157 - mcc: 0.6317 - val_loss: 0.7245 - val_accuracy: 0.7520 - val_recall: 0.6373 - val_precision: 0.8474 - val_AUROC: 0.9716 - val_AUPRC: 0.8425 - val_f1_score: 0.7275 - val_balanced_accuracy: 0.8123 - val_specificity: 0.9873 - val_miss_rate: 0.3627 - val_fall_out: 0.0127 - val_mcc: 0.7105
Epoch 11/100
63/63 [==============================] - 1s 9ms/step - loss: 0.8955 - accuracy: 0.7030 - recall: 0.5775 - precision: 0.8088 - AUROC: 0.9535 - AUPRC: 0.7762 - f1_score: 0.6739 - balanced_accuracy: 0.7812 - specificity: 0.9848 - miss_rate: 0.4225 - fall_out: 0.0152 - mcc: 0.6552 - val_loss: 0.6717 - val_accuracy: 0.7821 - val_recall: 0.6789 - val_precision: 0.8614 - val_AUROC: 0.9745 - val_AUPRC: 0.8615 - val_f1_score: 0.7593 - val_balanced_accuracy: 0.8334 - val_specificity: 0.9879 - val_miss_rate: 0.3211 - val_fall_out: 0.0121 - val_mcc: 0.7423
Epoch 12/100
63/63 [==============================] - 1s 10ms/step - loss: 0.8783 - accuracy: 0.7097 - recall: 0.5898 - precision: 0.8036 - AUROC: 0.9549 - AUPRC: 0.7815 - f1_score: 0.6803 - balanced_accuracy: 0.7869 - specificity: 0.9840 - miss_rate: 0.4102 - fall_out: 0.0160 - mcc: 0.6601 - val_loss: 0.6551 - val_accuracy: 0.7806 - val_recall: 0.6844 - val_precision: 0.8706 - val_AUROC: 0.9762 - val_AUPRC: 0.8683 - val_f1_score: 0.7663 - val_balanced_accuracy: 0.8365 - val_specificity: 0.9887 - val_miss_rate: 0.3156 - val_fall_out: 0.0113 - val_mcc: 0.7503
Epoch 13/100
63/63 [==============================] - 1s 10ms/step - loss: 0.8297 - accuracy: 0.7283 - recall: 0.6165 - precision: 0.8206 - AUROC: 0.9597 - AUPRC: 0.8010 - f1_score: 0.7040 - balanced_accuracy: 0.8008 - specificity: 0.9850 - miss_rate: 0.3835 - fall_out: 0.0150 - mcc: 0.6846 - val_loss: 0.6297 - val_accuracy: 0.7871 - val_recall: 0.7044 - val_precision: 0.8690 - val_AUROC: 0.9778 - val_AUPRC: 0.8750 - val_f1_score: 0.7781 - val_balanced_accuracy: 0.8463 - val_specificity: 0.9882 - val_miss_rate: 0.2956 - val_fall_out: 0.0118 - val_mcc: 0.7613
Epoch 14/100
63/63 [==============================] - 1s 10ms/step - loss: 0.7815 - accuracy: 0.7365 - recall: 0.6427 - precision: 0.8199 - AUROC: 0.9639 - AUPRC: 0.8162 - f1_score: 0.7205 - balanced_accuracy: 0.8135 - specificity: 0.9843 - miss_rate: 0.3573 - fall_out: 0.0157 - mcc: 0.6998 - val_loss: 0.6007 - val_accuracy: 0.7916 - val_recall: 0.7139 - val_precision: 0.8753 - val_AUROC: 0.9802 - val_AUPRC: 0.8843 - val_f1_score: 0.7864 - val_balanced_accuracy: 0.8513 - val_specificity: 0.9887 - val_miss_rate: 0.2861 - val_fall_out: 0.0113 - val_mcc: 0.7701
Epoch 15/100
63/63 [==============================] - 1s 10ms/step - loss: 0.7553 - accuracy: 0.7531 - recall: 0.6563 - precision: 0.8331 - AUROC: 0.9661 - AUPRC: 0.8294 - f1_score: 0.7342 - balanced_accuracy: 0.8209 - specificity: 0.9854 - miss_rate: 0.3437 - fall_out: 0.0146 - mcc: 0.7146 - val_loss: 0.5772 - val_accuracy: 0.8006 - val_recall: 0.7305 - val_precision: 0.8720 - val_AUROC: 0.9810 - val_AUPRC: 0.8916 - val_f1_score: 0.7950 - val_balanced_accuracy: 0.8593 - val_specificity: 0.9881 - val_miss_rate: 0.2695 - val_fall_out: 0.0119 - val_mcc: 0.7781
Epoch 16/100
63/63 [==============================] - 1s 10ms/step - loss: 0.7334 - accuracy: 0.7629 - recall: 0.6712 - precision: 0.8375 - AUROC: 0.9679 - AUPRC: 0.8395 - f1_score: 0.7452 - balanced_accuracy: 0.8284 - specificity: 0.9855 - miss_rate: 0.3288 - fall_out: 0.0145 - mcc: 0.7256 - val_loss: 0.5649 - val_accuracy: 0.8036 - val_recall: 0.7465 - val_precision: 0.8713 - val_AUROC: 0.9814 - val_AUPRC: 0.8936 - val_f1_score: 0.8041 - val_balanced_accuracy: 0.8671 - val_specificity: 0.9878 - val_miss_rate: 0.2535 - val_fall_out: 0.0122 - val_mcc: 0.7870
Epoch 17/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6967 - accuracy: 0.7727 - recall: 0.6924 - precision: 0.8427 - AUROC: 0.9707 - AUPRC: 0.8517 - f1_score: 0.7602 - balanced_accuracy: 0.8390 - specificity: 0.9856 - miss_rate: 0.3076 - fall_out: 0.0144 - mcc: 0.7407 - val_loss: 0.5412 - val_accuracy: 0.8191 - val_recall: 0.7595 - val_precision: 0.8824 - val_AUROC: 0.9833 - val_AUPRC: 0.9017 - val_f1_score: 0.8164 - val_balanced_accuracy: 0.8741 - val_specificity: 0.9888 - val_miss_rate: 0.2405 - val_fall_out: 0.0112 - val_mcc: 0.8004
Epoch 18/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6873 - accuracy: 0.7761 - recall: 0.6991 - precision: 0.8455 - AUROC: 0.9712 - AUPRC: 0.8515 - f1_score: 0.7654 - balanced_accuracy: 0.8425 - specificity: 0.9858 - miss_rate: 0.3009 - fall_out: 0.0142 - mcc: 0.7461 - val_loss: 0.5239 - val_accuracy: 0.8186 - val_recall: 0.7625 - val_precision: 0.8823 - val_AUROC: 0.9844 - val_AUPRC: 0.9077 - val_f1_score: 0.8181 - val_balanced_accuracy: 0.8756 - val_specificity: 0.9887 - val_miss_rate: 0.2375 - val_fall_out: 0.0113 - val_mcc: 0.8021
Epoch 19/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6512 - accuracy: 0.7901 - recall: 0.7204 - precision: 0.8548 - AUROC: 0.9742 - AUPRC: 0.8649 - f1_score: 0.7819 - balanced_accuracy: 0.8534 - specificity: 0.9864 - miss_rate: 0.2796 - fall_out: 0.0136 - mcc: 0.7633 - val_loss: 0.5205 - val_accuracy: 0.8241 - val_recall: 0.7690 - val_precision: 0.8781 - val_AUROC: 0.9841 - val_AUPRC: 0.9071 - val_f1_score: 0.8200 - val_balanced_accuracy: 0.8786 - val_specificity: 0.9881 - val_miss_rate: 0.2310 - val_fall_out: 0.0119 - val_mcc: 0.8036
Epoch 20/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6446 - accuracy: 0.7908 - recall: 0.7241 - precision: 0.8508 - AUROC: 0.9744 - AUPRC: 0.8668 - f1_score: 0.7823 - balanced_accuracy: 0.8550 - specificity: 0.9859 - miss_rate: 0.2759 - fall_out: 0.0141 - mcc: 0.7633 - val_loss: 0.5133 - val_accuracy: 0.8262 - val_recall: 0.7761 - val_precision: 0.8816 - val_AUROC: 0.9843 - val_AUPRC: 0.9081 - val_f1_score: 0.8255 - val_balanced_accuracy: 0.8822 - val_specificity: 0.9884 - val_miss_rate: 0.2239 - val_fall_out: 0.0116 - val_mcc: 0.8094
Epoch 21/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6262 - accuracy: 0.7986 - recall: 0.7231 - precision: 0.8614 - AUROC: 0.9758 - AUPRC: 0.8742 - f1_score: 0.7862 - balanced_accuracy: 0.8551 - specificity: 0.9871 - miss_rate: 0.2769 - fall_out: 0.0129 - mcc: 0.7683 - val_loss: 0.4816 - val_accuracy: 0.8332 - val_recall: 0.7836 - val_precision: 0.8886 - val_AUROC: 0.9860 - val_AUPRC: 0.9180 - val_f1_score: 0.8328 - val_balanced_accuracy: 0.8863 - val_specificity: 0.9891 - val_miss_rate: 0.2164 - val_fall_out: 0.0109 - val_mcc: 0.8175
Epoch 22/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6013 - accuracy: 0.8044 - recall: 0.7435 - precision: 0.8609 - AUROC: 0.9775 - AUPRC: 0.8822 - f1_score: 0.7979 - balanced_accuracy: 0.8651 - specificity: 0.9867 - miss_rate: 0.2565 - fall_out: 0.0133 - mcc: 0.7798 - val_loss: 0.4772 - val_accuracy: 0.8342 - val_recall: 0.7926 - val_precision: 0.8913 - val_AUROC: 0.9862 - val_AUPRC: 0.9188 - val_f1_score: 0.8390 - val_balanced_accuracy: 0.8909 - val_specificity: 0.9893 - val_miss_rate: 0.2074 - val_fall_out: 0.0107 - val_mcc: 0.8240
Epoch 23/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5883 - accuracy: 0.8094 - recall: 0.7492 - precision: 0.8676 - AUROC: 0.9780 - AUPRC: 0.8866 - f1_score: 0.8041 - balanced_accuracy: 0.8683 - specificity: 0.9873 - miss_rate: 0.2508 - fall_out: 0.0127 - mcc: 0.7866 - val_loss: 0.4696 - val_accuracy: 0.8397 - val_recall: 0.8011 - val_precision: 0.8908 - val_AUROC: 0.9862 - val_AUPRC: 0.9220 - val_f1_score: 0.8436 - val_balanced_accuracy: 0.8951 - val_specificity: 0.9891 - val_miss_rate: 0.1989 - val_fall_out: 0.0109 - val_mcc: 0.8286
Epoch 24/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5520 - accuracy: 0.8184 - recall: 0.7673 - precision: 0.8723 - AUROC: 0.9806 - AUPRC: 0.8961 - f1_score: 0.8164 - balanced_accuracy: 0.8774 - specificity: 0.9875 - miss_rate: 0.2327 - fall_out: 0.0125 - mcc: 0.7995 - val_loss: 0.4551 - val_accuracy: 0.8462 - val_recall: 0.8076 - val_precision: 0.8891 - val_AUROC: 0.9871 - val_AUPRC: 0.9255 - val_f1_score: 0.8464 - val_balanced_accuracy: 0.8982 - val_specificity: 0.9888 - val_miss_rate: 0.1924 - val_fall_out: 0.0112 - val_mcc: 0.8314
Epoch 25/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5417 - accuracy: 0.8216 - recall: 0.7720 - precision: 0.8732 - AUROC: 0.9813 - AUPRC: 0.9013 - f1_score: 0.8195 - balanced_accuracy: 0.8798 - specificity: 0.9875 - miss_rate: 0.2280 - fall_out: 0.0125 - mcc: 0.8027 - val_loss: 0.4433 - val_accuracy: 0.8482 - val_recall: 0.8061 - val_precision: 0.8894 - val_AUROC: 0.9877 - val_AUPRC: 0.9288 - val_f1_score: 0.8457 - val_balanced_accuracy: 0.8975 - val_specificity: 0.9889 - val_miss_rate: 0.1939 - val_fall_out: 0.0111 - val_mcc: 0.8307
Epoch 26/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5258 - accuracy: 0.8236 - recall: 0.7757 - precision: 0.8723 - AUROC: 0.9824 - AUPRC: 0.9053 - f1_score: 0.8211 - balanced_accuracy: 0.8815 - specificity: 0.9874 - miss_rate: 0.2243 - fall_out: 0.0126 - mcc: 0.8042 - val_loss: 0.4341 - val_accuracy: 0.8492 - val_recall: 0.8156 - val_precision: 0.8945 - val_AUROC: 0.9878 - val_AUPRC: 0.9320 - val_f1_score: 0.8532 - val_balanced_accuracy: 0.9025 - val_specificity: 0.9893 - val_miss_rate: 0.1844 - val_fall_out: 0.0107 - val_mcc: 0.8389
Epoch 27/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5151 - accuracy: 0.8318 - recall: 0.7893 - precision: 0.8767 - AUROC: 0.9825 - AUPRC: 0.9069 - f1_score: 0.8307 - balanced_accuracy: 0.8885 - specificity: 0.9877 - miss_rate: 0.2107 - fall_out: 0.0123 - mcc: 0.8144 - val_loss: 0.4323 - val_accuracy: 0.8567 - val_recall: 0.8176 - val_precision: 0.8952 - val_AUROC: 0.9882 - val_AUPRC: 0.9326 - val_f1_score: 0.8547 - val_balanced_accuracy: 0.9035 - val_specificity: 0.9894 - val_miss_rate: 0.1824 - val_fall_out: 0.0106 - val_mcc: 0.8404
Epoch 28/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5005 - accuracy: 0.8340 - recall: 0.7916 - precision: 0.8799 - AUROC: 0.9834 - AUPRC: 0.9116 - f1_score: 0.8334 - balanced_accuracy: 0.8898 - specificity: 0.9880 - miss_rate: 0.2084 - fall_out: 0.0120 - mcc: 0.8173 - val_loss: 0.4259 - val_accuracy: 0.8577 - val_recall: 0.8211 - val_precision: 0.8976 - val_AUROC: 0.9886 - val_AUPRC: 0.9326 - val_f1_score: 0.8577 - val_balanced_accuracy: 0.9054 - val_specificity: 0.9896 - val_miss_rate: 0.1789 - val_fall_out: 0.0104 - val_mcc: 0.8436
Epoch 29/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4895 - accuracy: 0.8394 - recall: 0.7967 - precision: 0.8808 - AUROC: 0.9840 - AUPRC: 0.9147 - f1_score: 0.8366 - balanced_accuracy: 0.8924 - specificity: 0.9880 - miss_rate: 0.2033 - fall_out: 0.0120 - mcc: 0.8208 - val_loss: 0.4134 - val_accuracy: 0.8562 - val_recall: 0.8211 - val_precision: 0.8883 - val_AUROC: 0.9890 - val_AUPRC: 0.9369 - val_f1_score: 0.8534 - val_balanced_accuracy: 0.9048 - val_specificity: 0.9885 - val_miss_rate: 0.1789 - val_fall_out: 0.0115 - val_mcc: 0.8386
Epoch 30/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4717 - accuracy: 0.8471 - recall: 0.8089 - precision: 0.8871 - AUROC: 0.9849 - AUPRC: 0.9198 - f1_score: 0.8462 - balanced_accuracy: 0.8987 - specificity: 0.9886 - miss_rate: 0.1911 - fall_out: 0.0114 - mcc: 0.8310 - val_loss: 0.4020 - val_accuracy: 0.8617 - val_recall: 0.8347 - val_precision: 0.8952 - val_AUROC: 0.9902 - val_AUPRC: 0.9403 - val_f1_score: 0.8639 - val_balanced_accuracy: 0.9119 - val_specificity: 0.9891 - val_miss_rate: 0.1653 - val_fall_out: 0.0109 - val_mcc: 0.8500
Epoch 31/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4719 - accuracy: 0.8473 - recall: 0.8045 - precision: 0.8884 - AUROC: 0.9851 - AUPRC: 0.9193 - f1_score: 0.8444 - balanced_accuracy: 0.8966 - specificity: 0.9888 - miss_rate: 0.1955 - fall_out: 0.0112 - mcc: 0.8293 - val_loss: 0.3982 - val_accuracy: 0.8617 - val_recall: 0.8332 - val_precision: 0.9018 - val_AUROC: 0.9895 - val_AUPRC: 0.9400 - val_f1_score: 0.8661 - val_balanced_accuracy: 0.9115 - val_specificity: 0.9899 - val_miss_rate: 0.1668 - val_fall_out: 0.0101 - val_mcc: 0.8527
Epoch 32/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4572 - accuracy: 0.8546 - recall: 0.8131 - precision: 0.8914 - AUROC: 0.9858 - AUPRC: 0.9240 - f1_score: 0.8505 - balanced_accuracy: 0.9011 - specificity: 0.9890 - miss_rate: 0.1869 - fall_out: 0.0110 - mcc: 0.8358 - val_loss: 0.4119 - val_accuracy: 0.8627 - val_recall: 0.8302 - val_precision: 0.9015 - val_AUROC: 0.9887 - val_AUPRC: 0.9368 - val_f1_score: 0.8644 - val_balanced_accuracy: 0.9100 - val_specificity: 0.9899 - val_miss_rate: 0.1698 - val_fall_out: 0.0101 - val_mcc: 0.8509
Epoch 33/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4440 - accuracy: 0.8562 - recall: 0.8204 - precision: 0.8970 - AUROC: 0.9869 - AUPRC: 0.9286 - f1_score: 0.8570 - balanced_accuracy: 0.9050 - specificity: 0.9895 - miss_rate: 0.1796 - fall_out: 0.0105 - mcc: 0.8429 - val_loss: 0.3976 - val_accuracy: 0.8712 - val_recall: 0.8397 - val_precision: 0.9040 - val_AUROC: 0.9897 - val_AUPRC: 0.9403 - val_f1_score: 0.8706 - val_balanced_accuracy: 0.9149 - val_specificity: 0.9901 - val_miss_rate: 0.1603 - val_fall_out: 0.0099 - val_mcc: 0.8576
Epoch 34/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4293 - accuracy: 0.8642 - recall: 0.8289 - precision: 0.8971 - AUROC: 0.9872 - AUPRC: 0.9300 - f1_score: 0.8617 - balanced_accuracy: 0.9092 - specificity: 0.9894 - miss_rate: 0.1711 - fall_out: 0.0106 - mcc: 0.8478 - val_loss: 0.3870 - val_accuracy: 0.8717 - val_recall: 0.8407 - val_precision: 0.9022 - val_AUROC: 0.9899 - val_AUPRC: 0.9415 - val_f1_score: 0.8703 - val_balanced_accuracy: 0.9153 - val_specificity: 0.9899 - val_miss_rate: 0.1593 - val_fall_out: 0.0101 - val_mcc: 0.8571
Epoch 35/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4069 - accuracy: 0.8721 - recall: 0.8349 - precision: 0.9031 - AUROC: 0.9886 - AUPRC: 0.9379 - f1_score: 0.8677 - balanced_accuracy: 0.9125 - specificity: 0.9900 - miss_rate: 0.1651 - fall_out: 0.0100 - mcc: 0.8544 - val_loss: 0.3824 - val_accuracy: 0.8732 - val_recall: 0.8532 - val_precision: 0.9054 - val_AUROC: 0.9891 - val_AUPRC: 0.9426 - val_f1_score: 0.8785 - val_balanced_accuracy: 0.9216 - val_specificity: 0.9901 - val_miss_rate: 0.1468 - val_fall_out: 0.0099 - val_mcc: 0.8659
Epoch 36/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3997 - accuracy: 0.8695 - recall: 0.8398 - precision: 0.9028 - AUROC: 0.9882 - AUPRC: 0.9374 - f1_score: 0.8702 - balanced_accuracy: 0.9149 - specificity: 0.9900 - miss_rate: 0.1602 - fall_out: 0.0100 - mcc: 0.8570 - val_loss: 0.3774 - val_accuracy: 0.8788 - val_recall: 0.8562 - val_precision: 0.9052 - val_AUROC: 0.9898 - val_AUPRC: 0.9449 - val_f1_score: 0.8800 - val_balanced_accuracy: 0.9231 - val_specificity: 0.9900 - val_miss_rate: 0.1438 - val_fall_out: 0.0100 - val_mcc: 0.8675
Epoch 37/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4121 - accuracy: 0.8686 - recall: 0.8386 - precision: 0.9013 - AUROC: 0.9884 - AUPRC: 0.9352 - f1_score: 0.8688 - balanced_accuracy: 0.9142 - specificity: 0.9898 - miss_rate: 0.1614 - fall_out: 0.0102 - mcc: 0.8555 - val_loss: 0.3741 - val_accuracy: 0.8763 - val_recall: 0.8502 - val_precision: 0.8998 - val_AUROC: 0.9898 - val_AUPRC: 0.9448 - val_f1_score: 0.8743 - val_balanced_accuracy: 0.9198 - val_specificity: 0.9895 - val_miss_rate: 0.1498 - val_fall_out: 0.0105 - val_mcc: 0.8612
Epoch 38/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3987 - accuracy: 0.8697 - recall: 0.8401 - precision: 0.9027 - AUROC: 0.9893 - AUPRC: 0.9403 - f1_score: 0.8702 - balanced_accuracy: 0.9150 - specificity: 0.9899 - miss_rate: 0.1599 - fall_out: 0.0101 - mcc: 0.8571 - val_loss: 0.3837 - val_accuracy: 0.8657 - val_recall: 0.8502 - val_precision: 0.8955 - val_AUROC: 0.9896 - val_AUPRC: 0.9439 - val_f1_score: 0.8723 - val_balanced_accuracy: 0.9196 - val_specificity: 0.9890 - val_miss_rate: 0.1498 - val_fall_out: 0.0110 - val_mcc: 0.8588
Epoch 39/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4017 - accuracy: 0.8741 - recall: 0.8411 - precision: 0.9049 - AUROC: 0.9885 - AUPRC: 0.9391 - f1_score: 0.8718 - balanced_accuracy: 0.9156 - specificity: 0.9902 - miss_rate: 0.1589 - fall_out: 0.0098 - mcc: 0.8588 - val_loss: 0.3726 - val_accuracy: 0.8758 - val_recall: 0.8547 - val_precision: 0.8984 - val_AUROC: 0.9895 - val_AUPRC: 0.9451 - val_f1_score: 0.8760 - val_balanced_accuracy: 0.9220 - val_specificity: 0.9893 - val_miss_rate: 0.1453 - val_fall_out: 0.0107 - val_mcc: 0.8629
Epoch 40/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3652 - accuracy: 0.8815 - recall: 0.8566 - precision: 0.9097 - AUROC: 0.9900 - AUPRC: 0.9469 - f1_score: 0.8823 - balanced_accuracy: 0.9236 - specificity: 0.9906 - miss_rate: 0.1434 - fall_out: 0.0094 - mcc: 0.8702 - val_loss: 0.3771 - val_accuracy: 0.8763 - val_recall: 0.8612 - val_precision: 0.9019 - val_AUROC: 0.9894 - val_AUPRC: 0.9448 - val_f1_score: 0.8811 - val_balanced_accuracy: 0.9254 - val_specificity: 0.9896 - val_miss_rate: 0.1388 - val_fall_out: 0.0104 - val_mcc: 0.8685
Epoch 41/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3718 - accuracy: 0.8799 - recall: 0.8548 - precision: 0.9105 - AUROC: 0.9897 - AUPRC: 0.9456 - f1_score: 0.8818 - balanced_accuracy: 0.9227 - specificity: 0.9907 - miss_rate: 0.1452 - fall_out: 0.0093 - mcc: 0.8696 - val_loss: 0.3630 - val_accuracy: 0.8818 - val_recall: 0.8607 - val_precision: 0.9014 - val_AUROC: 0.9902 - val_AUPRC: 0.9481 - val_f1_score: 0.8806 - val_balanced_accuracy: 0.9251 - val_specificity: 0.9895 - val_miss_rate: 0.1393 - val_fall_out: 0.0105 - val_mcc: 0.8679
Epoch 42/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3604 - accuracy: 0.8922 - recall: 0.8651 - precision: 0.9186 - AUROC: 0.9901 - AUPRC: 0.9485 - f1_score: 0.8911 - balanced_accuracy: 0.9283 - specificity: 0.9915 - miss_rate: 0.1349 - fall_out: 0.0085 - mcc: 0.8798 - val_loss: 0.3608 - val_accuracy: 0.8758 - val_recall: 0.8612 - val_precision: 0.8977 - val_AUROC: 0.9902 - val_AUPRC: 0.9476 - val_f1_score: 0.8791 - val_balanced_accuracy: 0.9252 - val_specificity: 0.9891 - val_miss_rate: 0.1388 - val_fall_out: 0.0109 - val_mcc: 0.8662
Epoch 43/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3586 - accuracy: 0.8861 - recall: 0.8618 - precision: 0.9107 - AUROC: 0.9907 - AUPRC: 0.9493 - f1_score: 0.8856 - balanced_accuracy: 0.9262 - specificity: 0.9906 - miss_rate: 0.1382 - fall_out: 0.0094 - mcc: 0.8737 - val_loss: 0.3534 - val_accuracy: 0.8863 - val_recall: 0.8692 - val_precision: 0.9084 - val_AUROC: 0.9897 - val_AUPRC: 0.9489 - val_f1_score: 0.8884 - val_balanced_accuracy: 0.9297 - val_specificity: 0.9903 - val_miss_rate: 0.1308 - val_fall_out: 0.0097 - val_mcc: 0.8765
Epoch 44/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3500 - accuracy: 0.8856 - recall: 0.8613 - precision: 0.9117 - AUROC: 0.9912 - AUPRC: 0.9522 - f1_score: 0.8858 - balanced_accuracy: 0.9260 - specificity: 0.9907 - miss_rate: 0.1387 - fall_out: 0.0093 - mcc: 0.8740 - val_loss: 0.3422 - val_accuracy: 0.8888 - val_recall: 0.8667 - val_precision: 0.9100 - val_AUROC: 0.9905 - val_AUPRC: 0.9520 - val_f1_score: 0.8879 - val_balanced_accuracy: 0.9286 - val_specificity: 0.9905 - val_miss_rate: 0.1333 - val_fall_out: 0.0095 - val_mcc: 0.8761
Epoch 45/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3425 - accuracy: 0.8930 - recall: 0.8684 - precision: 0.9178 - AUROC: 0.9910 - AUPRC: 0.9522 - f1_score: 0.8924 - balanced_accuracy: 0.9299 - specificity: 0.9914 - miss_rate: 0.1316 - fall_out: 0.0086 - mcc: 0.8812 - val_loss: 0.3541 - val_accuracy: 0.8833 - val_recall: 0.8677 - val_precision: 0.9026 - val_AUROC: 0.9905 - val_AUPRC: 0.9489 - val_f1_score: 0.8848 - val_balanced_accuracy: 0.9287 - val_specificity: 0.9896 - val_miss_rate: 0.1323 - val_fall_out: 0.0104 - val_mcc: 0.8725
Epoch 46/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3367 - accuracy: 0.8930 - recall: 0.8719 - precision: 0.9168 - AUROC: 0.9911 - AUPRC: 0.9534 - f1_score: 0.8938 - balanced_accuracy: 0.9315 - specificity: 0.9912 - miss_rate: 0.1281 - fall_out: 0.0088 - mcc: 0.8826 - val_loss: 0.3367 - val_accuracy: 0.8883 - val_recall: 0.8692 - val_precision: 0.9070 - val_AUROC: 0.9906 - val_AUPRC: 0.9522 - val_f1_score: 0.8877 - val_balanced_accuracy: 0.9297 - val_specificity: 0.9901 - val_miss_rate: 0.1308 - val_fall_out: 0.0099 - val_mcc: 0.8758
Epoch 47/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3287 - accuracy: 0.8980 - recall: 0.8747 - precision: 0.9223 - AUROC: 0.9917 - AUPRC: 0.9561 - f1_score: 0.8979 - balanced_accuracy: 0.9333 - specificity: 0.9918 - miss_rate: 0.1253 - fall_out: 0.0082 - mcc: 0.8873 - val_loss: 0.3501 - val_accuracy: 0.8828 - val_recall: 0.8687 - val_precision: 0.9088 - val_AUROC: 0.9903 - val_AUPRC: 0.9497 - val_f1_score: 0.8883 - val_balanced_accuracy: 0.9295 - val_specificity: 0.9903 - val_miss_rate: 0.1313 - val_fall_out: 0.0097 - val_mcc: 0.8765
Epoch 48/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3187 - accuracy: 0.8963 - recall: 0.8760 - precision: 0.9210 - AUROC: 0.9923 - AUPRC: 0.9591 - f1_score: 0.8979 - balanced_accuracy: 0.9338 - specificity: 0.9916 - miss_rate: 0.1240 - fall_out: 0.0084 - mcc: 0.8872 - val_loss: 0.3475 - val_accuracy: 0.8898 - val_recall: 0.8732 - val_precision: 0.9045 - val_AUROC: 0.9904 - val_AUPRC: 0.9511 - val_f1_score: 0.8886 - val_balanced_accuracy: 0.9315 - val_specificity: 0.9898 - val_miss_rate: 0.1268 - val_fall_out: 0.0102 - val_mcc: 0.8766
250/250 [==============================] - 1s 5ms/step - loss: 0.0946 - accuracy: 0.9726 - recall: 0.9636 - precision: 0.9824 - AUROC: 0.9996 - AUPRC: 0.9967 - f1_score: 0.9729 - balanced_accuracy: 0.9808 - specificity: 0.9981 - miss_rate: 0.0364 - fall_out: 0.0019 - mcc: 0.9699
63/63 [==============================] - 0s 5ms/step - loss: 0.3475 - accuracy: 0.8898 - recall: 0.8732 - precision: 0.9045 - AUROC: 0.9904 - AUPRC: 0.9511 - f1_score: 0.8886 - balanced_accuracy: 0.9315 - specificity: 0.9898 - miss_rate: 0.1268 - fall_out: 0.0102 - mcc: 0.8766
8it [04:20, 33.30s/it]
-- HOLDOUT 9 -- WINDOW window_3s
-- 6 Uncorrelated features: [Pearson+Spearman]
['mfcc16_mean', 'tempo', 'mfcc10_var', 'mfcc19_mean', 'mfcc11_var', 'mfcc17_mean']
-- Actually uncorrelated features: [confirmed via MIC]
[]
-- 1 Correlated features: [Pearson]
['spectral_centroid_mean']
Model: "MLP"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_260 (Dense) (None, 256) 14592
dropout_202 (Dropout) (None, 256) 0
dense_261 (Dense) (None, 256) 65792
dropout_203 (Dropout) (None, 256) 0
dense_262 (Dense) (None, 128) 32896
dropout_204 (Dropout) (None, 128) 0
dense_263 (Dense) (None, 128) 16512
dropout_205 (Dropout) (None, 128) 0
dense_264 (Dense) (None, 10) 1290
=================================================================
Total params: 131,082
Trainable params: 131,082
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 2s 19ms/step - loss: 2.2086 - accuracy: 0.2144 - recall: 0.0214 - precision: 0.4659 - AUROC: 0.6540 - AUPRC: 0.1933 - f1_score: 0.0410 - balanced_accuracy: 0.5093 - specificity: 0.9973 - miss_rate: 0.9786 - fall_out: 0.0027 - mcc: 0.0829 - val_loss: 1.6628 - val_accuracy: 0.4068 - val_recall: 0.1423 - val_precision: 0.8045 - val_AUROC: 0.8564 - val_AUPRC: 0.4609 - val_f1_score: 0.2418 - val_balanced_accuracy: 0.5692 - val_specificity: 0.9962 - val_miss_rate: 0.8577 - val_fall_out: 0.0038 - val_mcc: 0.3151
Epoch 2/100
63/63 [==============================] - 1s 10ms/step - loss: 1.7632 - accuracy: 0.3815 - recall: 0.1613 - precision: 0.6392 - AUROC: 0.8208 - AUPRC: 0.3885 - f1_score: 0.2576 - balanced_accuracy: 0.5756 - specificity: 0.9899 - miss_rate: 0.8387 - fall_out: 0.0101 - mcc: 0.2892 - val_loss: 1.3984 - val_accuracy: 0.5020 - val_recall: 0.2570 - val_precision: 0.7703 - val_AUROC: 0.8978 - val_AUPRC: 0.5654 - val_f1_score: 0.3854 - val_balanced_accuracy: 0.6242 - val_specificity: 0.9915 - val_miss_rate: 0.7430 - val_fall_out: 0.0085 - val_mcc: 0.4151
Epoch 3/100
63/63 [==============================] - 1s 10ms/step - loss: 1.5234 - accuracy: 0.4537 - recall: 0.2340 - precision: 0.6810 - AUROC: 0.8705 - AUPRC: 0.4864 - f1_score: 0.3483 - balanced_accuracy: 0.6109 - specificity: 0.9878 - miss_rate: 0.7660 - fall_out: 0.0122 - mcc: 0.3653 - val_loss: 1.2244 - val_accuracy: 0.5561 - val_recall: 0.3292 - val_precision: 0.7552 - val_AUROC: 0.9211 - val_AUPRC: 0.6311 - val_f1_score: 0.4585 - val_balanced_accuracy: 0.6587 - val_specificity: 0.9881 - val_miss_rate: 0.6708 - val_fall_out: 0.0119 - val_mcc: 0.4662
Epoch 4/100
63/63 [==============================] - 1s 10ms/step - loss: 1.3785 - accuracy: 0.5109 - recall: 0.2958 - precision: 0.6988 - AUROC: 0.8946 - AUPRC: 0.5514 - f1_score: 0.4157 - balanced_accuracy: 0.6408 - specificity: 0.9858 - miss_rate: 0.7042 - fall_out: 0.0142 - mcc: 0.4197 - val_loss: 1.1112 - val_accuracy: 0.6227 - val_recall: 0.3758 - val_precision: 0.8082 - val_AUROC: 0.9349 - val_AUPRC: 0.6843 - val_f1_score: 0.5130 - val_balanced_accuracy: 0.6829 - val_specificity: 0.9901 - val_miss_rate: 0.6242 - val_fall_out: 0.0099 - val_mcc: 0.5213
Epoch 5/100
63/63 [==============================] - 1s 10ms/step - loss: 1.2720 - accuracy: 0.5579 - recall: 0.3558 - precision: 0.7277 - AUROC: 0.9100 - AUPRC: 0.6042 - f1_score: 0.4780 - balanced_accuracy: 0.6705 - specificity: 0.9852 - miss_rate: 0.6442 - fall_out: 0.0148 - mcc: 0.4744 - val_loss: 0.9973 - val_accuracy: 0.6503 - val_recall: 0.4534 - val_precision: 0.8153 - val_AUROC: 0.9474 - val_AUPRC: 0.7342 - val_f1_score: 0.5827 - val_balanced_accuracy: 0.7210 - val_specificity: 0.9886 - val_miss_rate: 0.5466 - val_fall_out: 0.0114 - val_mcc: 0.5786
Epoch 6/100
63/63 [==============================] - 1s 10ms/step - loss: 1.1884 - accuracy: 0.5916 - recall: 0.4013 - precision: 0.7453 - AUROC: 0.9219 - AUPRC: 0.6477 - f1_score: 0.5217 - balanced_accuracy: 0.6930 - specificity: 0.9848 - miss_rate: 0.5987 - fall_out: 0.0152 - mcc: 0.5131 - val_loss: 0.9368 - val_accuracy: 0.6794 - val_recall: 0.4930 - val_precision: 0.8304 - val_AUROC: 0.9540 - val_AUPRC: 0.7631 - val_f1_score: 0.6187 - val_balanced_accuracy: 0.7409 - val_specificity: 0.9888 - val_miss_rate: 0.5070 - val_fall_out: 0.0112 - val_mcc: 0.6116
Epoch 7/100
63/63 [==============================] - 1s 10ms/step - loss: 1.1096 - accuracy: 0.6308 - recall: 0.4514 - precision: 0.7722 - AUROC: 0.9304 - AUPRC: 0.6860 - f1_score: 0.5698 - balanced_accuracy: 0.7183 - specificity: 0.9852 - miss_rate: 0.5486 - fall_out: 0.0148 - mcc: 0.5583 - val_loss: 0.8716 - val_accuracy: 0.6999 - val_recall: 0.5586 - val_precision: 0.8340 - val_AUROC: 0.9584 - val_AUPRC: 0.7890 - val_f1_score: 0.6691 - val_balanced_accuracy: 0.7731 - val_specificity: 0.9876 - val_miss_rate: 0.4414 - val_fall_out: 0.0124 - val_mcc: 0.6555
Epoch 8/100
63/63 [==============================] - 1s 10ms/step - loss: 1.0398 - accuracy: 0.6538 - recall: 0.4937 - precision: 0.7755 - AUROC: 0.9390 - AUPRC: 0.7116 - f1_score: 0.6034 - balanced_accuracy: 0.7389 - specificity: 0.9841 - miss_rate: 0.5063 - fall_out: 0.0159 - mcc: 0.5872 - val_loss: 0.8132 - val_accuracy: 0.7285 - val_recall: 0.5937 - val_precision: 0.8422 - val_AUROC: 0.9629 - val_AUPRC: 0.8090 - val_f1_score: 0.6964 - val_balanced_accuracy: 0.7907 - val_specificity: 0.9876 - val_miss_rate: 0.4063 - val_fall_out: 0.0124 - val_mcc: 0.6813
Epoch 9/100
63/63 [==============================] - 1s 10ms/step - loss: 0.9881 - accuracy: 0.6696 - recall: 0.5243 - precision: 0.7837 - AUROC: 0.9438 - AUPRC: 0.7384 - f1_score: 0.6283 - balanced_accuracy: 0.7541 - specificity: 0.9839 - miss_rate: 0.4757 - fall_out: 0.0161 - mcc: 0.6103 - val_loss: 0.7781 - val_accuracy: 0.7460 - val_recall: 0.6077 - val_precision: 0.8441 - val_AUROC: 0.9665 - val_AUPRC: 0.8238 - val_f1_score: 0.7067 - val_balanced_accuracy: 0.7976 - val_specificity: 0.9875 - val_miss_rate: 0.3923 - val_fall_out: 0.0125 - val_mcc: 0.6909
Epoch 10/100
63/63 [==============================] - 1s 10ms/step - loss: 0.9287 - accuracy: 0.6916 - recall: 0.5592 - precision: 0.7993 - AUROC: 0.9502 - AUPRC: 0.7618 - f1_score: 0.6581 - balanced_accuracy: 0.7718 - specificity: 0.9844 - miss_rate: 0.4408 - fall_out: 0.0156 - mcc: 0.6394 - val_loss: 0.7272 - val_accuracy: 0.7605 - val_recall: 0.6403 - val_precision: 0.8475 - val_AUROC: 0.9702 - val_AUPRC: 0.8409 - val_f1_score: 0.7295 - val_balanced_accuracy: 0.8137 - val_specificity: 0.9872 - val_miss_rate: 0.3597 - val_fall_out: 0.0128 - val_mcc: 0.7123
Epoch 11/100
63/63 [==============================] - 1s 10ms/step - loss: 0.8833 - accuracy: 0.7074 - recall: 0.5843 - precision: 0.8082 - AUROC: 0.9547 - AUPRC: 0.7811 - f1_score: 0.6782 - balanced_accuracy: 0.7844 - specificity: 0.9846 - miss_rate: 0.4157 - fall_out: 0.0154 - mcc: 0.6590 - val_loss: 0.7047 - val_accuracy: 0.7650 - val_recall: 0.6658 - val_precision: 0.8481 - val_AUROC: 0.9713 - val_AUPRC: 0.8479 - val_f1_score: 0.7460 - val_balanced_accuracy: 0.8263 - val_specificity: 0.9868 - val_miss_rate: 0.3342 - val_fall_out: 0.0132 - val_mcc: 0.7279
Epoch 12/100
63/63 [==============================] - 1s 10ms/step - loss: 0.8508 - accuracy: 0.7197 - recall: 0.6037 - precision: 0.8172 - AUROC: 0.9580 - AUPRC: 0.7937 - f1_score: 0.6944 - balanced_accuracy: 0.7944 - specificity: 0.9850 - miss_rate: 0.3963 - fall_out: 0.0150 - mcc: 0.6752 - val_loss: 0.6825 - val_accuracy: 0.7730 - val_recall: 0.6814 - val_precision: 0.8635 - val_AUROC: 0.9735 - val_AUPRC: 0.8573 - val_f1_score: 0.7617 - val_balanced_accuracy: 0.8347 - val_specificity: 0.9880 - val_miss_rate: 0.3186 - val_fall_out: 0.0120 - val_mcc: 0.7449
Epoch 13/100
63/63 [==============================] - 1s 10ms/step - loss: 0.8265 - accuracy: 0.7298 - recall: 0.6219 - precision: 0.8212 - AUROC: 0.9600 - AUPRC: 0.8039 - f1_score: 0.7078 - balanced_accuracy: 0.8034 - specificity: 0.9850 - miss_rate: 0.3781 - fall_out: 0.0150 - mcc: 0.6881 - val_loss: 0.6563 - val_accuracy: 0.7856 - val_recall: 0.6964 - val_precision: 0.8634 - val_AUROC: 0.9754 - val_AUPRC: 0.8665 - val_f1_score: 0.7709 - val_balanced_accuracy: 0.8421 - val_specificity: 0.9878 - val_miss_rate: 0.3036 - val_fall_out: 0.0122 - val_mcc: 0.7537
Epoch 14/100
63/63 [==============================] - 1s 10ms/step - loss: 0.7784 - accuracy: 0.7465 - recall: 0.6405 - precision: 0.8306 - AUROC: 0.9643 - AUPRC: 0.8226 - f1_score: 0.7233 - balanced_accuracy: 0.8130 - specificity: 0.9855 - miss_rate: 0.3595 - fall_out: 0.0145 - mcc: 0.7040 - val_loss: 0.6315 - val_accuracy: 0.7911 - val_recall: 0.7149 - val_precision: 0.8691 - val_AUROC: 0.9770 - val_AUPRC: 0.8755 - val_f1_score: 0.7845 - val_balanced_accuracy: 0.8515 - val_specificity: 0.9880 - val_miss_rate: 0.2851 - val_fall_out: 0.0120 - val_mcc: 0.7675
Epoch 15/100
63/63 [==============================] - 1s 10ms/step - loss: 0.7549 - accuracy: 0.7556 - recall: 0.6633 - precision: 0.8382 - AUROC: 0.9656 - AUPRC: 0.8301 - f1_score: 0.7406 - balanced_accuracy: 0.8246 - specificity: 0.9858 - miss_rate: 0.3367 - fall_out: 0.0142 - mcc: 0.7214 - val_loss: 0.6085 - val_accuracy: 0.8031 - val_recall: 0.7280 - val_precision: 0.8727 - val_AUROC: 0.9784 - val_AUPRC: 0.8817 - val_f1_score: 0.7938 - val_balanced_accuracy: 0.8581 - val_specificity: 0.9882 - val_miss_rate: 0.2720 - val_fall_out: 0.0118 - val_mcc: 0.7770
Epoch 16/100
63/63 [==============================] - 1s 10ms/step - loss: 0.7327 - accuracy: 0.7609 - recall: 0.6732 - precision: 0.8376 - AUROC: 0.9680 - AUPRC: 0.8378 - f1_score: 0.7465 - balanced_accuracy: 0.8294 - specificity: 0.9855 - miss_rate: 0.3268 - fall_out: 0.0145 - mcc: 0.7269 - val_loss: 0.5880 - val_accuracy: 0.8036 - val_recall: 0.7490 - val_precision: 0.8753 - val_AUROC: 0.9801 - val_AUPRC: 0.8880 - val_f1_score: 0.8072 - val_balanced_accuracy: 0.8686 - val_specificity: 0.9881 - val_miss_rate: 0.2510 - val_fall_out: 0.0119 - val_mcc: 0.7906
Epoch 17/100
63/63 [==============================] - 1s 10ms/step - loss: 0.7002 - accuracy: 0.7702 - recall: 0.6885 - precision: 0.8435 - AUROC: 0.9705 - AUPRC: 0.8493 - f1_score: 0.7582 - balanced_accuracy: 0.8372 - specificity: 0.9858 - miss_rate: 0.3115 - fall_out: 0.0142 - mcc: 0.7388 - val_loss: 0.5682 - val_accuracy: 0.8031 - val_recall: 0.7505 - val_precision: 0.8740 - val_AUROC: 0.9815 - val_AUPRC: 0.8946 - val_f1_score: 0.8075 - val_balanced_accuracy: 0.8692 - val_specificity: 0.9880 - val_miss_rate: 0.2495 - val_fall_out: 0.0120 - val_mcc: 0.7907
Epoch 18/100
63/63 [==============================] - 1s 11ms/step - loss: 0.6843 - accuracy: 0.7807 - recall: 0.6978 - precision: 0.8490 - AUROC: 0.9718 - AUPRC: 0.8539 - f1_score: 0.7660 - balanced_accuracy: 0.8420 - specificity: 0.9862 - miss_rate: 0.3022 - fall_out: 0.0138 - mcc: 0.7471 - val_loss: 0.5573 - val_accuracy: 0.8116 - val_recall: 0.7635 - val_precision: 0.8754 - val_AUROC: 0.9818 - val_AUPRC: 0.8979 - val_f1_score: 0.8156 - val_balanced_accuracy: 0.8757 - val_specificity: 0.9879 - val_miss_rate: 0.2365 - val_fall_out: 0.0121 - val_mcc: 0.7989
Epoch 19/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6546 - accuracy: 0.7861 - recall: 0.7153 - precision: 0.8516 - AUROC: 0.9738 - AUPRC: 0.8638 - f1_score: 0.7775 - balanced_accuracy: 0.8507 - specificity: 0.9862 - miss_rate: 0.2847 - fall_out: 0.0138 - mcc: 0.7587 - val_loss: 0.5440 - val_accuracy: 0.8151 - val_recall: 0.7690 - val_precision: 0.8697 - val_AUROC: 0.9827 - val_AUPRC: 0.9016 - val_f1_score: 0.8163 - val_balanced_accuracy: 0.8781 - val_specificity: 0.9872 - val_miss_rate: 0.2310 - val_fall_out: 0.0128 - val_mcc: 0.7991
Epoch 20/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6444 - accuracy: 0.7938 - recall: 0.7272 - precision: 0.8589 - AUROC: 0.9741 - AUPRC: 0.8674 - f1_score: 0.7876 - balanced_accuracy: 0.8570 - specificity: 0.9867 - miss_rate: 0.2728 - fall_out: 0.0133 - mcc: 0.7693 - val_loss: 0.5405 - val_accuracy: 0.8166 - val_recall: 0.7725 - val_precision: 0.8722 - val_AUROC: 0.9820 - val_AUPRC: 0.9026 - val_f1_score: 0.8193 - val_balanced_accuracy: 0.8800 - val_specificity: 0.9874 - val_miss_rate: 0.2275 - val_fall_out: 0.0126 - val_mcc: 0.8024
Epoch 21/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5988 - accuracy: 0.8029 - recall: 0.7355 - precision: 0.8634 - AUROC: 0.9779 - AUPRC: 0.8826 - f1_score: 0.7943 - balanced_accuracy: 0.8613 - specificity: 0.9871 - miss_rate: 0.2645 - fall_out: 0.0129 - mcc: 0.7765 - val_loss: 0.5249 - val_accuracy: 0.8196 - val_recall: 0.7811 - val_precision: 0.8695 - val_AUROC: 0.9835 - val_AUPRC: 0.9078 - val_f1_score: 0.8229 - val_balanced_accuracy: 0.8840 - val_specificity: 0.9870 - val_miss_rate: 0.2189 - val_fall_out: 0.0130 - val_mcc: 0.8058
Epoch 22/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5895 - accuracy: 0.8099 - recall: 0.7496 - precision: 0.8648 - AUROC: 0.9783 - AUPRC: 0.8876 - f1_score: 0.8031 - balanced_accuracy: 0.8683 - specificity: 0.9870 - miss_rate: 0.2504 - fall_out: 0.0130 - mcc: 0.7854 - val_loss: 0.5161 - val_accuracy: 0.8191 - val_recall: 0.7816 - val_precision: 0.8681 - val_AUROC: 0.9844 - val_AUPRC: 0.9097 - val_f1_score: 0.8226 - val_balanced_accuracy: 0.8842 - val_specificity: 0.9868 - val_miss_rate: 0.2184 - val_fall_out: 0.0132 - val_mcc: 0.8053
Epoch 23/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5694 - accuracy: 0.8168 - recall: 0.7569 - precision: 0.8675 - AUROC: 0.9796 - AUPRC: 0.8904 - f1_score: 0.8084 - balanced_accuracy: 0.8720 - specificity: 0.9872 - miss_rate: 0.2431 - fall_out: 0.0128 - mcc: 0.7910 - val_loss: 0.5027 - val_accuracy: 0.8246 - val_recall: 0.7901 - val_precision: 0.8698 - val_AUROC: 0.9850 - val_AUPRC: 0.9140 - val_f1_score: 0.8280 - val_balanced_accuracy: 0.8885 - val_specificity: 0.9869 - val_miss_rate: 0.2099 - val_fall_out: 0.0131 - val_mcc: 0.8111
Epoch 24/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5638 - accuracy: 0.8223 - recall: 0.7670 - precision: 0.8751 - AUROC: 0.9798 - AUPRC: 0.8939 - f1_score: 0.8175 - balanced_accuracy: 0.8774 - specificity: 0.9878 - miss_rate: 0.2330 - fall_out: 0.0122 - mcc: 0.8008 - val_loss: 0.4779 - val_accuracy: 0.8337 - val_recall: 0.7956 - val_precision: 0.8783 - val_AUROC: 0.9865 - val_AUPRC: 0.9207 - val_f1_score: 0.8349 - val_balanced_accuracy: 0.8917 - val_specificity: 0.9878 - val_miss_rate: 0.2044 - val_fall_out: 0.0122 - val_mcc: 0.8188
Epoch 25/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5427 - accuracy: 0.8292 - recall: 0.7777 - precision: 0.8821 - AUROC: 0.9807 - AUPRC: 0.9000 - f1_score: 0.8266 - balanced_accuracy: 0.8831 - specificity: 0.9884 - miss_rate: 0.2223 - fall_out: 0.0116 - mcc: 0.8106 - val_loss: 0.4792 - val_accuracy: 0.8367 - val_recall: 0.8091 - val_precision: 0.8816 - val_AUROC: 0.9858 - val_AUPRC: 0.9205 - val_f1_score: 0.8438 - val_balanced_accuracy: 0.8985 - val_specificity: 0.9879 - val_miss_rate: 0.1909 - val_fall_out: 0.0121 - val_mcc: 0.8282
Epoch 26/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5327 - accuracy: 0.8277 - recall: 0.7793 - precision: 0.8763 - AUROC: 0.9817 - AUPRC: 0.9033 - f1_score: 0.8250 - balanced_accuracy: 0.8835 - specificity: 0.9878 - miss_rate: 0.2207 - fall_out: 0.0122 - mcc: 0.8085 - val_loss: 0.4768 - val_accuracy: 0.8332 - val_recall: 0.8056 - val_precision: 0.8806 - val_AUROC: 0.9857 - val_AUPRC: 0.9213 - val_f1_score: 0.8414 - val_balanced_accuracy: 0.8967 - val_specificity: 0.9879 - val_miss_rate: 0.1944 - val_fall_out: 0.0121 - val_mcc: 0.8257
Epoch 27/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5101 - accuracy: 0.8354 - recall: 0.7903 - precision: 0.8815 - AUROC: 0.9827 - AUPRC: 0.9095 - f1_score: 0.8334 - balanced_accuracy: 0.8893 - specificity: 0.9882 - miss_rate: 0.2097 - fall_out: 0.0118 - mcc: 0.8175 - val_loss: 0.4629 - val_accuracy: 0.8487 - val_recall: 0.8136 - val_precision: 0.8889 - val_AUROC: 0.9862 - val_AUPRC: 0.9240 - val_f1_score: 0.8496 - val_balanced_accuracy: 0.9012 - val_specificity: 0.9887 - val_miss_rate: 0.1864 - val_fall_out: 0.0113 - val_mcc: 0.8347
Epoch 28/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5091 - accuracy: 0.8363 - recall: 0.7907 - precision: 0.8822 - AUROC: 0.9830 - AUPRC: 0.9097 - f1_score: 0.8339 - balanced_accuracy: 0.8895 - specificity: 0.9883 - miss_rate: 0.2093 - fall_out: 0.0117 - mcc: 0.8181 - val_loss: 0.4456 - val_accuracy: 0.8527 - val_recall: 0.8166 - val_precision: 0.8946 - val_AUROC: 0.9877 - val_AUPRC: 0.9292 - val_f1_score: 0.8539 - val_balanced_accuracy: 0.9030 - val_specificity: 0.9893 - val_miss_rate: 0.1834 - val_fall_out: 0.0107 - val_mcc: 0.8395
Epoch 29/100
63/63 [==============================] - 1s 9ms/step - loss: 0.4811 - accuracy: 0.8458 - recall: 0.8020 - precision: 0.8887 - AUROC: 0.9845 - AUPRC: 0.9197 - f1_score: 0.8431 - balanced_accuracy: 0.8954 - specificity: 0.9888 - miss_rate: 0.1980 - fall_out: 0.0112 - mcc: 0.8280 - val_loss: 0.4343 - val_accuracy: 0.8552 - val_recall: 0.8257 - val_precision: 0.8913 - val_AUROC: 0.9880 - val_AUPRC: 0.9322 - val_f1_score: 0.8572 - val_balanced_accuracy: 0.9072 - val_specificity: 0.9888 - val_miss_rate: 0.1743 - val_fall_out: 0.0112 - val_mcc: 0.8428
Epoch 30/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4683 - accuracy: 0.8446 - recall: 0.8064 - precision: 0.8876 - AUROC: 0.9854 - AUPRC: 0.9211 - f1_score: 0.8450 - balanced_accuracy: 0.8975 - specificity: 0.9887 - miss_rate: 0.1936 - fall_out: 0.0113 - mcc: 0.8299 - val_loss: 0.4375 - val_accuracy: 0.8552 - val_recall: 0.8246 - val_precision: 0.8951 - val_AUROC: 0.9871 - val_AUPRC: 0.9319 - val_f1_score: 0.8584 - val_balanced_accuracy: 0.9070 - val_specificity: 0.9893 - val_miss_rate: 0.1754 - val_fall_out: 0.0107 - val_mcc: 0.8443
Epoch 31/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4579 - accuracy: 0.8546 - recall: 0.8112 - precision: 0.8925 - AUROC: 0.9859 - AUPRC: 0.9227 - f1_score: 0.8499 - balanced_accuracy: 0.9002 - specificity: 0.9891 - miss_rate: 0.1888 - fall_out: 0.0109 - mcc: 0.8353 - val_loss: 0.4340 - val_accuracy: 0.8582 - val_recall: 0.8297 - val_precision: 0.8856 - val_AUROC: 0.9875 - val_AUPRC: 0.9316 - val_f1_score: 0.8567 - val_balanced_accuracy: 0.9089 - val_specificity: 0.9881 - val_miss_rate: 0.1703 - val_fall_out: 0.0119 - val_mcc: 0.8419
Epoch 32/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4511 - accuracy: 0.8533 - recall: 0.8204 - precision: 0.8916 - AUROC: 0.9859 - AUPRC: 0.9251 - f1_score: 0.8545 - balanced_accuracy: 0.9047 - specificity: 0.9889 - miss_rate: 0.1796 - fall_out: 0.0111 - mcc: 0.8400 - val_loss: 0.4144 - val_accuracy: 0.8652 - val_recall: 0.8397 - val_precision: 0.8939 - val_AUROC: 0.9890 - val_AUPRC: 0.9368 - val_f1_score: 0.8659 - val_balanced_accuracy: 0.9143 - val_specificity: 0.9889 - val_miss_rate: 0.1603 - val_fall_out: 0.0111 - val_mcc: 0.8521
Epoch 33/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4441 - accuracy: 0.8555 - recall: 0.8174 - precision: 0.8924 - AUROC: 0.9869 - AUPRC: 0.9277 - f1_score: 0.8532 - balanced_accuracy: 0.9032 - specificity: 0.9890 - miss_rate: 0.1826 - fall_out: 0.0110 - mcc: 0.8387 - val_loss: 0.4125 - val_accuracy: 0.8617 - val_recall: 0.8292 - val_precision: 0.8907 - val_AUROC: 0.9888 - val_AUPRC: 0.9379 - val_f1_score: 0.8588 - val_balanced_accuracy: 0.9089 - val_specificity: 0.9887 - val_miss_rate: 0.1708 - val_fall_out: 0.0113 - val_mcc: 0.8444
Epoch 34/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4466 - accuracy: 0.8543 - recall: 0.8163 - precision: 0.8901 - AUROC: 0.9868 - AUPRC: 0.9276 - f1_score: 0.8516 - balanced_accuracy: 0.9025 - specificity: 0.9888 - miss_rate: 0.1837 - fall_out: 0.0112 - mcc: 0.8368 - val_loss: 0.3990 - val_accuracy: 0.8637 - val_recall: 0.8417 - val_precision: 0.8941 - val_AUROC: 0.9894 - val_AUPRC: 0.9415 - val_f1_score: 0.8671 - val_balanced_accuracy: 0.9153 - val_specificity: 0.9889 - val_miss_rate: 0.1583 - val_fall_out: 0.0111 - val_mcc: 0.8533
Epoch 35/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4195 - accuracy: 0.8661 - recall: 0.8298 - precision: 0.9017 - AUROC: 0.9882 - AUPRC: 0.9342 - f1_score: 0.8643 - balanced_accuracy: 0.9099 - specificity: 0.9900 - miss_rate: 0.1702 - fall_out: 0.0100 - mcc: 0.8508 - val_loss: 0.4139 - val_accuracy: 0.8607 - val_recall: 0.8422 - val_precision: 0.8927 - val_AUROC: 0.9887 - val_AUPRC: 0.9375 - val_f1_score: 0.8667 - val_balanced_accuracy: 0.9155 - val_specificity: 0.9888 - val_miss_rate: 0.1578 - val_fall_out: 0.0112 - val_mcc: 0.8528
Epoch 36/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4101 - accuracy: 0.8659 - recall: 0.8349 - precision: 0.9007 - AUROC: 0.9888 - AUPRC: 0.9361 - f1_score: 0.8666 - balanced_accuracy: 0.9123 - specificity: 0.9898 - miss_rate: 0.1651 - fall_out: 0.0102 - mcc: 0.8531 - val_loss: 0.4034 - val_accuracy: 0.8672 - val_recall: 0.8472 - val_precision: 0.8952 - val_AUROC: 0.9885 - val_AUPRC: 0.9392 - val_f1_score: 0.8705 - val_balanced_accuracy: 0.9181 - val_specificity: 0.9890 - val_miss_rate: 0.1528 - val_fall_out: 0.0110 - val_mcc: 0.8570
250/250 [==============================] - 1s 5ms/step - loss: 0.1604 - accuracy: 0.9545 - recall: 0.9386 - precision: 0.9722 - AUROC: 0.9987 - AUPRC: 0.9898 - f1_score: 0.9551 - balanced_accuracy: 0.9678 - specificity: 0.9970 - miss_rate: 0.0614 - fall_out: 0.0030 - mcc: 0.9504
63/63 [==============================] - 0s 5ms/step - loss: 0.4034 - accuracy: 0.8672 - recall: 0.8472 - precision: 0.8952 - AUROC: 0.9885 - AUPRC: 0.9392 - f1_score: 0.8705 - balanced_accuracy: 0.9181 - specificity: 0.9890 - miss_rate: 0.1528 - fall_out: 0.0110 - mcc: 0.8570
9it [04:51, 32.50s/it]
-- HOLDOUT 10 -- WINDOW window_3s
-- 6 Uncorrelated features: [Pearson+Spearman]
['mfcc16_mean', 'tempo', 'mfcc10_var', 'mfcc19_mean', 'mfcc11_var', 'mfcc17_mean']
-- Actually uncorrelated features: [confirmed via MIC]
[]
-- 1 Correlated features: [Pearson]
['spectral_centroid_mean']
Model: "MLP"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_265 (Dense) (None, 256) 14592
dropout_206 (Dropout) (None, 256) 0
dense_266 (Dense) (None, 256) 65792
dropout_207 (Dropout) (None, 256) 0
dense_267 (Dense) (None, 128) 32896
dropout_208 (Dropout) (None, 128) 0
dense_268 (Dense) (None, 128) 16512
dropout_209 (Dropout) (None, 128) 0
dense_269 (Dense) (None, 10) 1290
=================================================================
Total params: 131,082
Trainable params: 131,082
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 2s 19ms/step - loss: 2.1676 - accuracy: 0.2365 - recall: 0.0427 - precision: 0.5303 - AUROC: 0.6726 - AUPRC: 0.2177 - f1_score: 0.0791 - balanced_accuracy: 0.5193 - specificity: 0.9958 - miss_rate: 0.9573 - fall_out: 0.0042 - mcc: 0.1292 - val_loss: 1.6747 - val_accuracy: 0.4088 - val_recall: 0.1473 - val_precision: 0.7840 - val_AUROC: 0.8484 - val_AUPRC: 0.4460 - val_f1_score: 0.2480 - val_balanced_accuracy: 0.5714 - val_specificity: 0.9955 - val_miss_rate: 0.8527 - val_fall_out: 0.0045 - val_mcc: 0.3155
Epoch 2/100
63/63 [==============================] - 1s 10ms/step - loss: 1.7390 - accuracy: 0.3695 - recall: 0.1527 - precision: 0.6044 - AUROC: 0.8240 - AUPRC: 0.3884 - f1_score: 0.2438 - balanced_accuracy: 0.5708 - specificity: 0.9889 - miss_rate: 0.8473 - fall_out: 0.0111 - mcc: 0.2707 - val_loss: 1.4114 - val_accuracy: 0.4910 - val_recall: 0.2370 - val_precision: 0.7679 - val_AUROC: 0.8959 - val_AUPRC: 0.5490 - val_f1_score: 0.3622 - val_balanced_accuracy: 0.6145 - val_specificity: 0.9920 - val_miss_rate: 0.7630 - val_fall_out: 0.0080 - val_mcc: 0.3973
Epoch 3/100
63/63 [==============================] - 1s 10ms/step - loss: 1.5481 - accuracy: 0.4481 - recall: 0.2158 - precision: 0.6584 - AUROC: 0.8640 - AUPRC: 0.4722 - f1_score: 0.3251 - balanced_accuracy: 0.6017 - specificity: 0.9876 - miss_rate: 0.7842 - fall_out: 0.0124 - mcc: 0.3426 - val_loss: 1.2300 - val_accuracy: 0.5621 - val_recall: 0.3116 - val_precision: 0.8217 - val_AUROC: 0.9226 - val_AUPRC: 0.6428 - val_f1_score: 0.4519 - val_balanced_accuracy: 0.6521 - val_specificity: 0.9925 - val_miss_rate: 0.6884 - val_fall_out: 0.0075 - val_mcc: 0.4776
Epoch 4/100
63/63 [==============================] - 1s 10ms/step - loss: 1.3978 - accuracy: 0.5009 - recall: 0.2869 - precision: 0.6926 - AUROC: 0.8913 - AUPRC: 0.5447 - f1_score: 0.4058 - balanced_accuracy: 0.6364 - specificity: 0.9858 - miss_rate: 0.7131 - fall_out: 0.0142 - mcc: 0.4107 - val_loss: 1.1394 - val_accuracy: 0.6067 - val_recall: 0.3662 - val_precision: 0.8068 - val_AUROC: 0.9326 - val_AUPRC: 0.6798 - val_f1_score: 0.5038 - val_balanced_accuracy: 0.6782 - val_specificity: 0.9903 - val_miss_rate: 0.6338 - val_fall_out: 0.0097 - val_mcc: 0.5138
Epoch 5/100
63/63 [==============================] - 1s 10ms/step - loss: 1.2825 - accuracy: 0.5516 - recall: 0.3447 - precision: 0.7176 - AUROC: 0.9082 - AUPRC: 0.5970 - f1_score: 0.4657 - balanced_accuracy: 0.6648 - specificity: 0.9849 - miss_rate: 0.6553 - fall_out: 0.0151 - mcc: 0.4624 - val_loss: 1.0167 - val_accuracy: 0.6663 - val_recall: 0.4163 - val_precision: 0.8385 - val_AUROC: 0.9477 - val_AUPRC: 0.7382 - val_f1_score: 0.5564 - val_balanced_accuracy: 0.7037 - val_specificity: 0.9911 - val_miss_rate: 0.5837 - val_fall_out: 0.0089 - val_mcc: 0.5627
Epoch 6/100
63/63 [==============================] - 1s 10ms/step - loss: 1.1856 - accuracy: 0.5932 - recall: 0.3939 - precision: 0.7495 - AUROC: 0.9213 - AUPRC: 0.6443 - f1_score: 0.5164 - balanced_accuracy: 0.6896 - specificity: 0.9854 - miss_rate: 0.6061 - fall_out: 0.0146 - mcc: 0.5099 - val_loss: 0.9318 - val_accuracy: 0.6909 - val_recall: 0.4920 - val_precision: 0.8379 - val_AUROC: 0.9541 - val_AUPRC: 0.7662 - val_f1_score: 0.6199 - val_balanced_accuracy: 0.7407 - val_specificity: 0.9894 - val_miss_rate: 0.5080 - val_fall_out: 0.0106 - val_mcc: 0.6143
Epoch 7/100
63/63 [==============================] - 1s 10ms/step - loss: 1.1022 - accuracy: 0.6202 - recall: 0.4517 - precision: 0.7601 - AUROC: 0.9322 - AUPRC: 0.6842 - f1_score: 0.5666 - balanced_accuracy: 0.7179 - specificity: 0.9842 - miss_rate: 0.5483 - fall_out: 0.0158 - mcc: 0.5530 - val_loss: 0.8724 - val_accuracy: 0.7169 - val_recall: 0.5306 - val_precision: 0.8432 - val_AUROC: 0.9598 - val_AUPRC: 0.7935 - val_f1_score: 0.6513 - val_balanced_accuracy: 0.7598 - val_specificity: 0.9890 - val_miss_rate: 0.4694 - val_fall_out: 0.0110 - val_mcc: 0.6419
Epoch 8/100
63/63 [==============================] - 1s 10ms/step - loss: 1.0445 - accuracy: 0.6484 - recall: 0.4842 - precision: 0.7747 - AUROC: 0.9383 - AUPRC: 0.7107 - f1_score: 0.5960 - balanced_accuracy: 0.7343 - specificity: 0.9844 - miss_rate: 0.5158 - fall_out: 0.0156 - mcc: 0.5807 - val_loss: 0.8229 - val_accuracy: 0.7315 - val_recall: 0.5631 - val_precision: 0.8509 - val_AUROC: 0.9640 - val_AUPRC: 0.8124 - val_f1_score: 0.6777 - val_balanced_accuracy: 0.7761 - val_specificity: 0.9890 - val_miss_rate: 0.4369 - val_fall_out: 0.0110 - val_mcc: 0.6663
Epoch 9/100
63/63 [==============================] - 1s 10ms/step - loss: 0.9937 - accuracy: 0.6614 - recall: 0.5140 - precision: 0.7862 - AUROC: 0.9441 - AUPRC: 0.7319 - f1_score: 0.6216 - balanced_accuracy: 0.7492 - specificity: 0.9845 - miss_rate: 0.4860 - fall_out: 0.0155 - mcc: 0.6050 - val_loss: 0.7670 - val_accuracy: 0.7450 - val_recall: 0.6122 - val_precision: 0.8618 - val_AUROC: 0.9680 - val_AUPRC: 0.8322 - val_f1_score: 0.7159 - val_balanced_accuracy: 0.8007 - val_specificity: 0.9891 - val_miss_rate: 0.3878 - val_fall_out: 0.0109 - val_mcc: 0.7022
Epoch 10/100
63/63 [==============================] - 1s 10ms/step - loss: 0.9518 - accuracy: 0.6851 - recall: 0.5452 - precision: 0.7910 - AUROC: 0.9481 - AUPRC: 0.7507 - f1_score: 0.6455 - balanced_accuracy: 0.7646 - specificity: 0.9840 - miss_rate: 0.4548 - fall_out: 0.0160 - mcc: 0.6267 - val_loss: 0.7308 - val_accuracy: 0.7665 - val_recall: 0.6403 - val_precision: 0.8706 - val_AUROC: 0.9702 - val_AUPRC: 0.8480 - val_f1_score: 0.7379 - val_balanced_accuracy: 0.8149 - val_specificity: 0.9894 - val_miss_rate: 0.3597 - val_fall_out: 0.0106 - val_mcc: 0.7237
Epoch 11/100
63/63 [==============================] - 1s 10ms/step - loss: 0.8865 - accuracy: 0.7087 - recall: 0.5787 - precision: 0.8097 - AUROC: 0.9547 - AUPRC: 0.7788 - f1_score: 0.6749 - balanced_accuracy: 0.7818 - specificity: 0.9849 - miss_rate: 0.4213 - fall_out: 0.0151 - mcc: 0.6563 - val_loss: 0.6906 - val_accuracy: 0.7781 - val_recall: 0.6784 - val_precision: 0.8624 - val_AUROC: 0.9726 - val_AUPRC: 0.8582 - val_f1_score: 0.7594 - val_balanced_accuracy: 0.8332 - val_specificity: 0.9880 - val_miss_rate: 0.3216 - val_fall_out: 0.0120 - val_mcc: 0.7426
Epoch 12/100
63/63 [==============================] - 1s 10ms/step - loss: 0.8390 - accuracy: 0.7211 - recall: 0.6102 - precision: 0.8159 - AUROC: 0.9596 - AUPRC: 0.7975 - f1_score: 0.6982 - balanced_accuracy: 0.7975 - specificity: 0.9847 - miss_rate: 0.3898 - fall_out: 0.0153 - mcc: 0.6785 - val_loss: 0.6556 - val_accuracy: 0.7836 - val_recall: 0.6909 - val_precision: 0.8662 - val_AUROC: 0.9755 - val_AUPRC: 0.8704 - val_f1_score: 0.7687 - val_balanced_accuracy: 0.8395 - val_specificity: 0.9881 - val_miss_rate: 0.3091 - val_fall_out: 0.0119 - val_mcc: 0.7519
Epoch 13/100
63/63 [==============================] - 1s 10ms/step - loss: 0.8151 - accuracy: 0.7310 - recall: 0.6253 - precision: 0.8200 - AUROC: 0.9613 - AUPRC: 0.8068 - f1_score: 0.7095 - balanced_accuracy: 0.8050 - specificity: 0.9847 - miss_rate: 0.3747 - fall_out: 0.0153 - mcc: 0.6895 - val_loss: 0.6464 - val_accuracy: 0.7871 - val_recall: 0.7029 - val_precision: 0.8650 - val_AUROC: 0.9757 - val_AUPRC: 0.8729 - val_f1_score: 0.7756 - val_balanced_accuracy: 0.8454 - val_specificity: 0.9878 - val_miss_rate: 0.2971 - val_fall_out: 0.0122 - val_mcc: 0.7584
Epoch 14/100
63/63 [==============================] - 1s 10ms/step - loss: 0.7904 - accuracy: 0.7429 - recall: 0.6455 - precision: 0.8274 - AUROC: 0.9633 - AUPRC: 0.8169 - f1_score: 0.7253 - balanced_accuracy: 0.8153 - specificity: 0.9850 - miss_rate: 0.3545 - fall_out: 0.0150 - mcc: 0.7053 - val_loss: 0.6149 - val_accuracy: 0.7946 - val_recall: 0.7280 - val_precision: 0.8669 - val_AUROC: 0.9774 - val_AUPRC: 0.8829 - val_f1_score: 0.7914 - val_balanced_accuracy: 0.8578 - val_specificity: 0.9876 - val_miss_rate: 0.2720 - val_fall_out: 0.0124 - val_mcc: 0.7740
Epoch 15/100
63/63 [==============================] - 1s 10ms/step - loss: 0.7511 - accuracy: 0.7500 - recall: 0.6613 - precision: 0.8318 - AUROC: 0.9663 - AUPRC: 0.8305 - f1_score: 0.7368 - balanced_accuracy: 0.8232 - specificity: 0.9851 - miss_rate: 0.3387 - fall_out: 0.0149 - mcc: 0.7169 - val_loss: 0.5984 - val_accuracy: 0.8126 - val_recall: 0.7315 - val_precision: 0.8785 - val_AUROC: 0.9790 - val_AUPRC: 0.8881 - val_f1_score: 0.7983 - val_balanced_accuracy: 0.8601 - val_specificity: 0.9888 - val_miss_rate: 0.2685 - val_fall_out: 0.0112 - val_mcc: 0.7820
Epoch 16/100
63/63 [==============================] - 1s 10ms/step - loss: 0.7195 - accuracy: 0.7619 - recall: 0.6765 - precision: 0.8378 - AUROC: 0.9692 - AUPRC: 0.8412 - f1_score: 0.7485 - balanced_accuracy: 0.8310 - specificity: 0.9854 - miss_rate: 0.3235 - fall_out: 0.0146 - mcc: 0.7289 - val_loss: 0.5832 - val_accuracy: 0.8116 - val_recall: 0.7445 - val_precision: 0.8783 - val_AUROC: 0.9795 - val_AUPRC: 0.8917 - val_f1_score: 0.8059 - val_balanced_accuracy: 0.8665 - val_specificity: 0.9885 - val_miss_rate: 0.2555 - val_fall_out: 0.0115 - val_mcc: 0.7895
Epoch 17/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6899 - accuracy: 0.7791 - recall: 0.6991 - precision: 0.8530 - AUROC: 0.9708 - AUPRC: 0.8539 - f1_score: 0.7684 - balanced_accuracy: 0.8429 - specificity: 0.9866 - miss_rate: 0.3009 - fall_out: 0.0134 - mcc: 0.7500 - val_loss: 0.5486 - val_accuracy: 0.8111 - val_recall: 0.7590 - val_precision: 0.8818 - val_AUROC: 0.9826 - val_AUPRC: 0.9024 - val_f1_score: 0.8158 - val_balanced_accuracy: 0.8739 - val_specificity: 0.9887 - val_miss_rate: 0.2410 - val_fall_out: 0.0113 - val_mcc: 0.7998
Epoch 18/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6589 - accuracy: 0.7871 - recall: 0.7139 - precision: 0.8518 - AUROC: 0.9731 - AUPRC: 0.8626 - f1_score: 0.7768 - balanced_accuracy: 0.8501 - specificity: 0.9862 - miss_rate: 0.2861 - fall_out: 0.0138 - mcc: 0.7579 - val_loss: 0.5413 - val_accuracy: 0.8206 - val_recall: 0.7756 - val_precision: 0.8856 - val_AUROC: 0.9819 - val_AUPRC: 0.9039 - val_f1_score: 0.8269 - val_balanced_accuracy: 0.8822 - val_specificity: 0.9889 - val_miss_rate: 0.2244 - val_fall_out: 0.0111 - val_mcc: 0.8113
Epoch 19/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6457 - accuracy: 0.7888 - recall: 0.7209 - precision: 0.8510 - AUROC: 0.9740 - AUPRC: 0.8648 - f1_score: 0.7806 - balanced_accuracy: 0.8535 - specificity: 0.9860 - miss_rate: 0.2791 - fall_out: 0.0140 - mcc: 0.7616 - val_loss: 0.5156 - val_accuracy: 0.8287 - val_recall: 0.7836 - val_precision: 0.8881 - val_AUROC: 0.9838 - val_AUPRC: 0.9116 - val_f1_score: 0.8326 - val_balanced_accuracy: 0.8863 - val_specificity: 0.9890 - val_miss_rate: 0.2164 - val_fall_out: 0.0110 - val_mcc: 0.8172
Epoch 20/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6267 - accuracy: 0.7985 - recall: 0.7335 - precision: 0.8570 - AUROC: 0.9758 - AUPRC: 0.8742 - f1_score: 0.7904 - balanced_accuracy: 0.8599 - specificity: 0.9864 - miss_rate: 0.2665 - fall_out: 0.0136 - mcc: 0.7720 - val_loss: 0.5076 - val_accuracy: 0.8292 - val_recall: 0.7816 - val_precision: 0.8869 - val_AUROC: 0.9847 - val_AUPRC: 0.9136 - val_f1_score: 0.8309 - val_balanced_accuracy: 0.8852 - val_specificity: 0.9889 - val_miss_rate: 0.2184 - val_fall_out: 0.0111 - val_mcc: 0.8154
Epoch 21/100
63/63 [==============================] - 1s 10ms/step - loss: 0.6025 - accuracy: 0.8095 - recall: 0.7441 - precision: 0.8684 - AUROC: 0.9773 - AUPRC: 0.8820 - f1_score: 0.8015 - balanced_accuracy: 0.8658 - specificity: 0.9875 - miss_rate: 0.2559 - fall_out: 0.0125 - mcc: 0.7841 - val_loss: 0.4929 - val_accuracy: 0.8372 - val_recall: 0.7886 - val_precision: 0.8958 - val_AUROC: 0.9858 - val_AUPRC: 0.9188 - val_f1_score: 0.8388 - val_balanced_accuracy: 0.8892 - val_specificity: 0.9898 - val_miss_rate: 0.2114 - val_fall_out: 0.0102 - val_mcc: 0.8242
Epoch 22/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5879 - accuracy: 0.8149 - recall: 0.7497 - precision: 0.8725 - AUROC: 0.9781 - AUPRC: 0.8866 - f1_score: 0.8065 - balanced_accuracy: 0.8688 - specificity: 0.9878 - miss_rate: 0.2503 - fall_out: 0.0122 - mcc: 0.7895 - val_loss: 0.4854 - val_accuracy: 0.8357 - val_recall: 0.7946 - val_precision: 0.8910 - val_AUROC: 0.9859 - val_AUPRC: 0.9199 - val_f1_score: 0.8400 - val_balanced_accuracy: 0.8919 - val_specificity: 0.9892 - val_miss_rate: 0.2054 - val_fall_out: 0.0108 - val_mcc: 0.8250
Epoch 23/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5610 - accuracy: 0.8221 - recall: 0.7650 - precision: 0.8744 - AUROC: 0.9800 - AUPRC: 0.8940 - f1_score: 0.8161 - balanced_accuracy: 0.8764 - specificity: 0.9878 - miss_rate: 0.2350 - fall_out: 0.0122 - mcc: 0.7993 - val_loss: 0.4710 - val_accuracy: 0.8417 - val_recall: 0.8006 - val_precision: 0.8893 - val_AUROC: 0.9862 - val_AUPRC: 0.9216 - val_f1_score: 0.8426 - val_balanced_accuracy: 0.8948 - val_specificity: 0.9889 - val_miss_rate: 0.1994 - val_fall_out: 0.0111 - val_mcc: 0.8275
Epoch 24/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5360 - accuracy: 0.8252 - recall: 0.7729 - precision: 0.8777 - AUROC: 0.9815 - AUPRC: 0.9010 - f1_score: 0.8220 - balanced_accuracy: 0.8805 - specificity: 0.9880 - miss_rate: 0.2271 - fall_out: 0.0120 - mcc: 0.8056 - val_loss: 0.4607 - val_accuracy: 0.8422 - val_recall: 0.8131 - val_precision: 0.8937 - val_AUROC: 0.9866 - val_AUPRC: 0.9252 - val_f1_score: 0.8515 - val_balanced_accuracy: 0.9012 - val_specificity: 0.9893 - val_miss_rate: 0.1869 - val_fall_out: 0.0107 - val_mcc: 0.8370
Epoch 25/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5265 - accuracy: 0.8329 - recall: 0.7844 - precision: 0.8822 - AUROC: 0.9825 - AUPRC: 0.9067 - f1_score: 0.8305 - balanced_accuracy: 0.8864 - specificity: 0.9884 - miss_rate: 0.2156 - fall_out: 0.0116 - mcc: 0.8146 - val_loss: 0.4510 - val_accuracy: 0.8467 - val_recall: 0.8106 - val_precision: 0.8905 - val_AUROC: 0.9877 - val_AUPRC: 0.9298 - val_f1_score: 0.8487 - val_balanced_accuracy: 0.8998 - val_specificity: 0.9889 - val_miss_rate: 0.1894 - val_fall_out: 0.0111 - val_mcc: 0.8339
Epoch 26/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5080 - accuracy: 0.8389 - recall: 0.7895 - precision: 0.8829 - AUROC: 0.9828 - AUPRC: 0.9107 - f1_score: 0.8336 - balanced_accuracy: 0.8889 - specificity: 0.9884 - miss_rate: 0.2105 - fall_out: 0.0116 - mcc: 0.8178 - val_loss: 0.4397 - val_accuracy: 0.8547 - val_recall: 0.8252 - val_precision: 0.8932 - val_AUROC: 0.9879 - val_AUPRC: 0.9317 - val_f1_score: 0.8578 - val_balanced_accuracy: 0.9071 - val_specificity: 0.9890 - val_miss_rate: 0.1748 - val_fall_out: 0.0110 - val_mcc: 0.8435
Epoch 27/100
63/63 [==============================] - 1s 10ms/step - loss: 0.5003 - accuracy: 0.8402 - recall: 0.7966 - precision: 0.8841 - AUROC: 0.9832 - AUPRC: 0.9126 - f1_score: 0.8381 - balanced_accuracy: 0.8925 - specificity: 0.9884 - miss_rate: 0.2034 - fall_out: 0.0116 - mcc: 0.8225 - val_loss: 0.4375 - val_accuracy: 0.8572 - val_recall: 0.8191 - val_precision: 0.8984 - val_AUROC: 0.9882 - val_AUPRC: 0.9317 - val_f1_score: 0.8569 - val_balanced_accuracy: 0.9044 - val_specificity: 0.9897 - val_miss_rate: 0.1809 - val_fall_out: 0.0103 - val_mcc: 0.8429
Epoch 28/100
63/63 [==============================] - 1s 9ms/step - loss: 0.4935 - accuracy: 0.8432 - recall: 0.7951 - precision: 0.8823 - AUROC: 0.9837 - AUPRC: 0.9131 - f1_score: 0.8364 - balanced_accuracy: 0.8917 - specificity: 0.9882 - miss_rate: 0.2049 - fall_out: 0.0118 - mcc: 0.8206 - val_loss: 0.4377 - val_accuracy: 0.8567 - val_recall: 0.8216 - val_precision: 0.8962 - val_AUROC: 0.9878 - val_AUPRC: 0.9322 - val_f1_score: 0.8573 - val_balanced_accuracy: 0.9055 - val_specificity: 0.9894 - val_miss_rate: 0.1784 - val_fall_out: 0.0106 - val_mcc: 0.8432
Epoch 29/100
63/63 [==============================] - 1s 9ms/step - loss: 0.4796 - accuracy: 0.8436 - recall: 0.8012 - precision: 0.8847 - AUROC: 0.9847 - AUPRC: 0.9191 - f1_score: 0.8409 - balanced_accuracy: 0.8948 - specificity: 0.9884 - miss_rate: 0.1988 - fall_out: 0.0116 - mcc: 0.8254 - val_loss: 0.4239 - val_accuracy: 0.8527 - val_recall: 0.8231 - val_precision: 0.8905 - val_AUROC: 0.9891 - val_AUPRC: 0.9345 - val_f1_score: 0.8555 - val_balanced_accuracy: 0.9060 - val_specificity: 0.9888 - val_miss_rate: 0.1769 - val_fall_out: 0.0112 - val_mcc: 0.8409
Epoch 30/100
63/63 [==============================] - 1s 9ms/step - loss: 0.4583 - accuracy: 0.8531 - recall: 0.8105 - precision: 0.8874 - AUROC: 0.9858 - AUPRC: 0.9250 - f1_score: 0.8472 - balanced_accuracy: 0.8995 - specificity: 0.9886 - miss_rate: 0.1895 - fall_out: 0.0114 - mcc: 0.8321 - val_loss: 0.4132 - val_accuracy: 0.8632 - val_recall: 0.8332 - val_precision: 0.9053 - val_AUROC: 0.9895 - val_AUPRC: 0.9379 - val_f1_score: 0.8677 - val_balanced_accuracy: 0.9117 - val_specificity: 0.9903 - val_miss_rate: 0.1668 - val_fall_out: 0.0097 - val_mcc: 0.8546
Epoch 31/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4424 - accuracy: 0.8591 - recall: 0.8191 - precision: 0.8971 - AUROC: 0.9867 - AUPRC: 0.9276 - f1_score: 0.8564 - balanced_accuracy: 0.9044 - specificity: 0.9896 - miss_rate: 0.1809 - fall_out: 0.0104 - mcc: 0.8423 - val_loss: 0.4083 - val_accuracy: 0.8722 - val_recall: 0.8432 - val_precision: 0.9019 - val_AUROC: 0.9889 - val_AUPRC: 0.9390 - val_f1_score: 0.8716 - val_balanced_accuracy: 0.9165 - val_specificity: 0.9898 - val_miss_rate: 0.1568 - val_fall_out: 0.0102 - val_mcc: 0.8584
Epoch 32/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4337 - accuracy: 0.8567 - recall: 0.8209 - precision: 0.8945 - AUROC: 0.9874 - AUPRC: 0.9308 - f1_score: 0.8561 - balanced_accuracy: 0.9051 - specificity: 0.9892 - miss_rate: 0.1791 - fall_out: 0.0108 - mcc: 0.8418 - val_loss: 0.4074 - val_accuracy: 0.8627 - val_recall: 0.8347 - val_precision: 0.8952 - val_AUROC: 0.9887 - val_AUPRC: 0.9395 - val_f1_score: 0.8639 - val_balanced_accuracy: 0.9119 - val_specificity: 0.9891 - val_miss_rate: 0.1653 - val_fall_out: 0.0109 - val_mcc: 0.8500
Epoch 33/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4227 - accuracy: 0.8671 - recall: 0.8345 - precision: 0.8988 - AUROC: 0.9875 - AUPRC: 0.9317 - f1_score: 0.8655 - balanced_accuracy: 0.9121 - specificity: 0.9896 - miss_rate: 0.1655 - fall_out: 0.0104 - mcc: 0.8519 - val_loss: 0.3938 - val_accuracy: 0.8697 - val_recall: 0.8402 - val_precision: 0.8987 - val_AUROC: 0.9901 - val_AUPRC: 0.9432 - val_f1_score: 0.8685 - val_balanced_accuracy: 0.9148 - val_specificity: 0.9895 - val_miss_rate: 0.1598 - val_fall_out: 0.0105 - val_mcc: 0.8550
Epoch 34/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4322 - accuracy: 0.8662 - recall: 0.8319 - precision: 0.9016 - AUROC: 0.9867 - AUPRC: 0.9318 - f1_score: 0.8654 - balanced_accuracy: 0.9109 - specificity: 0.9899 - miss_rate: 0.1681 - fall_out: 0.0101 - mcc: 0.8519 - val_loss: 0.3984 - val_accuracy: 0.8717 - val_recall: 0.8452 - val_precision: 0.9055 - val_AUROC: 0.9896 - val_AUPRC: 0.9413 - val_f1_score: 0.8743 - val_balanced_accuracy: 0.9177 - val_specificity: 0.9902 - val_miss_rate: 0.1548 - val_fall_out: 0.0098 - val_mcc: 0.8615
Epoch 35/100
63/63 [==============================] - 1s 9ms/step - loss: 0.4243 - accuracy: 0.8642 - recall: 0.8309 - precision: 0.8988 - AUROC: 0.9873 - AUPRC: 0.9332 - f1_score: 0.8635 - balanced_accuracy: 0.9103 - specificity: 0.9896 - miss_rate: 0.1691 - fall_out: 0.0104 - mcc: 0.8498 - val_loss: 0.3841 - val_accuracy: 0.8753 - val_recall: 0.8467 - val_precision: 0.9028 - val_AUROC: 0.9900 - val_AUPRC: 0.9450 - val_f1_score: 0.8738 - val_balanced_accuracy: 0.9183 - val_specificity: 0.9899 - val_miss_rate: 0.1533 - val_fall_out: 0.0101 - val_mcc: 0.8609
Epoch 36/100
63/63 [==============================] - 1s 10ms/step - loss: 0.4133 - accuracy: 0.8674 - recall: 0.8368 - precision: 0.8993 - AUROC: 0.9878 - AUPRC: 0.9352 - f1_score: 0.8669 - balanced_accuracy: 0.9132 - specificity: 0.9896 - miss_rate: 0.1632 - fall_out: 0.0104 - mcc: 0.8534 - val_loss: 0.3757 - val_accuracy: 0.8763 - val_recall: 0.8512 - val_precision: 0.9076 - val_AUROC: 0.9905 - val_AUPRC: 0.9466 - val_f1_score: 0.8785 - val_balanced_accuracy: 0.9208 - val_specificity: 0.9904 - val_miss_rate: 0.1488 - val_fall_out: 0.0096 - val_mcc: 0.8660
Epoch 37/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3885 - accuracy: 0.8753 - recall: 0.8456 - precision: 0.9073 - AUROC: 0.9896 - AUPRC: 0.9421 - f1_score: 0.8753 - balanced_accuracy: 0.9180 - specificity: 0.9904 - miss_rate: 0.1544 - fall_out: 0.0096 - mcc: 0.8627 - val_loss: 0.3744 - val_accuracy: 0.8722 - val_recall: 0.8557 - val_precision: 0.9023 - val_AUROC: 0.9902 - val_AUPRC: 0.9468 - val_f1_score: 0.8784 - val_balanced_accuracy: 0.9227 - val_specificity: 0.9897 - val_miss_rate: 0.1443 - val_fall_out: 0.0103 - val_mcc: 0.8656
Epoch 38/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3676 - accuracy: 0.8799 - recall: 0.8535 - precision: 0.9093 - AUROC: 0.9906 - AUPRC: 0.9481 - f1_score: 0.8805 - balanced_accuracy: 0.9220 - specificity: 0.9905 - miss_rate: 0.1465 - fall_out: 0.0095 - mcc: 0.8682 - val_loss: 0.3646 - val_accuracy: 0.8838 - val_recall: 0.8607 - val_precision: 0.9104 - val_AUROC: 0.9910 - val_AUPRC: 0.9506 - val_f1_score: 0.8849 - val_balanced_accuracy: 0.9257 - val_specificity: 0.9906 - val_miss_rate: 0.1393 - val_fall_out: 0.0094 - val_mcc: 0.8729
Epoch 39/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3726 - accuracy: 0.8819 - recall: 0.8541 - precision: 0.9076 - AUROC: 0.9899 - AUPRC: 0.9457 - f1_score: 0.8800 - balanced_accuracy: 0.9222 - specificity: 0.9903 - miss_rate: 0.1459 - fall_out: 0.0097 - mcc: 0.8677 - val_loss: 0.3530 - val_accuracy: 0.8758 - val_recall: 0.8547 - val_precision: 0.9074 - val_AUROC: 0.9917 - val_AUPRC: 0.9527 - val_f1_score: 0.8803 - val_balanced_accuracy: 0.9225 - val_specificity: 0.9903 - val_miss_rate: 0.1453 - val_fall_out: 0.0097 - val_mcc: 0.8679
Epoch 40/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3571 - accuracy: 0.8829 - recall: 0.8592 - precision: 0.9086 - AUROC: 0.9910 - AUPRC: 0.9494 - f1_score: 0.8832 - balanced_accuracy: 0.9248 - specificity: 0.9904 - miss_rate: 0.1408 - fall_out: 0.0096 - mcc: 0.8711 - val_loss: 0.3501 - val_accuracy: 0.8878 - val_recall: 0.8652 - val_precision: 0.9133 - val_AUROC: 0.9915 - val_AUPRC: 0.9530 - val_f1_score: 0.8886 - val_balanced_accuracy: 0.9281 - val_specificity: 0.9909 - val_miss_rate: 0.1348 - val_fall_out: 0.0091 - val_mcc: 0.8770
Epoch 41/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3637 - accuracy: 0.8841 - recall: 0.8588 - precision: 0.9110 - AUROC: 0.9900 - AUPRC: 0.9471 - f1_score: 0.8841 - balanced_accuracy: 0.9248 - specificity: 0.9907 - miss_rate: 0.1412 - fall_out: 0.0093 - mcc: 0.8722 - val_loss: 0.3548 - val_accuracy: 0.8783 - val_recall: 0.8607 - val_precision: 0.9047 - val_AUROC: 0.9919 - val_AUPRC: 0.9525 - val_f1_score: 0.8822 - val_balanced_accuracy: 0.9253 - val_specificity: 0.9899 - val_miss_rate: 0.1393 - val_fall_out: 0.0101 - val_mcc: 0.8698
Epoch 42/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3600 - accuracy: 0.8883 - recall: 0.8608 - precision: 0.9130 - AUROC: 0.9904 - AUPRC: 0.9488 - f1_score: 0.8862 - balanced_accuracy: 0.9259 - specificity: 0.9909 - miss_rate: 0.1392 - fall_out: 0.0091 - mcc: 0.8744 - val_loss: 0.3439 - val_accuracy: 0.8803 - val_recall: 0.8607 - val_precision: 0.9076 - val_AUROC: 0.9924 - val_AUPRC: 0.9541 - val_f1_score: 0.8835 - val_balanced_accuracy: 0.9255 - val_specificity: 0.9903 - val_miss_rate: 0.1393 - val_fall_out: 0.0097 - val_mcc: 0.8713
Epoch 43/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3349 - accuracy: 0.8937 - recall: 0.8680 - precision: 0.9179 - AUROC: 0.9918 - AUPRC: 0.9544 - f1_score: 0.8922 - balanced_accuracy: 0.9297 - specificity: 0.9914 - miss_rate: 0.1320 - fall_out: 0.0086 - mcc: 0.8811 - val_loss: 0.3472 - val_accuracy: 0.8848 - val_recall: 0.8702 - val_precision: 0.9033 - val_AUROC: 0.9912 - val_AUPRC: 0.9536 - val_f1_score: 0.8865 - val_balanced_accuracy: 0.9299 - val_specificity: 0.9896 - val_miss_rate: 0.1298 - val_fall_out: 0.0104 - val_mcc: 0.8743
Epoch 44/100
63/63 [==============================] - 1s 10ms/step - loss: 0.3538 - accuracy: 0.8863 - recall: 0.8647 - precision: 0.9132 - AUROC: 0.9904 - AUPRC: 0.9502 - f1_score: 0.8883 - balanced_accuracy: 0.9278 - specificity: 0.9909 - miss_rate: 0.1353 - fall_out: 0.0091 - mcc: 0.8767 - val_loss: 0.3462 - val_accuracy: 0.8828 - val_recall: 0.8627 - val_precision: 0.9073 - val_AUROC: 0.9910 - val_AUPRC: 0.9527 - val_f1_score: 0.8844 - val_balanced_accuracy: 0.9265 - val_specificity: 0.9902 - val_miss_rate: 0.1373 - val_fall_out: 0.0098 - val_mcc: 0.8723
250/250 [==============================] - 1s 5ms/step - loss: 0.1089 - accuracy: 0.9692 - recall: 0.9589 - precision: 0.9769 - AUROC: 0.9993 - AUPRC: 0.9952 - f1_score: 0.9678 - balanced_accuracy: 0.9782 - specificity: 0.9975 - miss_rate: 0.0411 - fall_out: 0.0025 - mcc: 0.9643
63/63 [==============================] - 0s 5ms/step - loss: 0.3462 - accuracy: 0.8828 - recall: 0.8627 - precision: 0.9073 - AUROC: 0.9910 - AUPRC: 0.9527 - f1_score: 0.8844 - balanced_accuracy: 0.9265 - specificity: 0.9902 - miss_rate: 0.1373 - fall_out: 0.0098 - mcc: 0.8723
10it [05:26, 32.65s/it]
for window_type in ("window_30s", "window_3s"):
MLP_metrics_estimate = model_metrics_holdout_estimate(MLP_fixed_metrics[window_type], number_of_splits)
print(f"-- WINDOW {window_type} --")
print(f"MLP Fixed Metrics - {number_of_splits}-holdouts estimate:")
print(f"Accuracy : train - {MLP_metrics_estimate['accuracy_train']} -- test - {MLP_metrics_estimate['accuracy_test']}")
print(f"AUROC : train - {MLP_metrics_estimate['AUROC_train']} -- test - {MLP_metrics_estimate['AUROC_test']}")
print(f"AUPRC : train - {MLP_metrics_estimate['AUPRC_train']} -- test - {MLP_metrics_estimate['AUPRC_test']}")
print("-"*80)
print("MLP - Train history:")
plot_train_history(MLP_fixed_history[window_type])
print("-"*100)
-- WINDOW window_30s -- MLP Fixed Metrics - 10-holdouts estimate: Accuracy : train - 0.7496871054172516 -- test - 0.6410000026226044 AUROC : train - 0.9698648989200592 -- test - 0.9367441713809967 AUPRC : train - 0.8321779370307922 -- test - 0.7057618498802185 -------------------------------------------------------------------------------- MLP - Train history:
---------------------------------------------------------------------------------------------------- -- WINDOW window_3s -- MLP Fixed Metrics - 10-holdouts estimate: Accuracy : train - 0.961811113357544 -- test - 0.8786072194576263 AUROC : train - 0.9990216016769409 -- test - 0.9896780610084533 AUPRC : train - 0.9928601801395416 -- test - 0.9466524302959443 -------------------------------------------------------------------------------- MLP - Train history:
----------------------------------------------------------------------------------------------------
print("---- Tuned MLP ----")
input_data = {"window_30s": data['features_30s'], "window_3s": data['features_3s']}
data_labels = {"window_30s": labels_30s, "window_3s": labels_3s}
MLP_tuned_metrics = {}
MLP_tuned_history = {}
MLP_tuned_metrics["window_30s"] = []
MLP_tuned_metrics["window_3s"] = []
MLP_tuned_history["window_30s"] = []
MLP_tuned_history["window_3s"] = []
for window_type in ("window_30s", "window_3s"):
# Generate holdouts
for holdout_number, (train_indices, test_indices) in enumerate(tqdm(holdouts_generator.split(input_data[window_type], data_labels[window_type]))):
print(f"-- HOLDOUT {holdout_number+1} -- WINDOW {window_type}")
# Train/Test data
x_train, x_test = input_data[window_type].iloc[train_indices], input_data[window_type].iloc[test_indices]
y_train, y_test = data_labels[window_type].iloc[train_indices], data_labels[window_type].iloc[test_indices]
## Hyperparameter tuning
# Generate holdouts
for holdout_number_tuning, (train_indices_tuning, val_indices_tuning) in enumerate(holdouts_generator_tuning.split(x_train, y_train)):
# Train/Validation data
x_train_tuning, x_val_tuning = x_train.iloc[train_indices_tuning], x_train.iloc[val_indices_tuning]
y_train_tuning, y_val_tuning = y_train.iloc[train_indices_tuning], y_train.iloc[val_indices_tuning]
# One-hot encoding
y_train_tuning = one_hot_encoding(y_train_tuning, 10)
y_val_tuning = one_hot_encoding(y_val_tuning, 10)
hp = kt.HyperParameters()
best_hyperparameters = hyperparameter_tuning(
x_train_tuning.values,
x_val_tuning.values,
y_train_tuning.values,
y_val_tuning.values,
build_MLP_hypermodel,
name = "MLP_hypermodel_" + str(holdout_number) + "_" + str(window_type),
directory='MLP_hypermodel',
max_trials = 8,
epochs = 50,
batch_size = 128
)
## Remove uncorrelated features with the output
uncorrelated_features = uncorrelated_features_test(x_train, y_train)
for feature in (x_train.columns):
if feature in (uncorrelated_features):
x_train = x_train.drop(columns=feature)
x_test = x_test.drop(columns=feature)
## Remove correlated features with eachother
correlated_features = correlated_features_test(x_train)
for feature in (x_train.columns):
if feature in (correlated_features):
x_train = x_train.drop(columns=feature)
x_test = x_test.drop(columns=feature)
# One-hot encoding
y_train = one_hot_encoding(y_train, 10)
y_test = one_hot_encoding(y_test, 10)
# Build MLP with best set of hyperparameters
MLP = build_MLP(best_hyperparameters, x_train.shape)
print("- Training model:\n")
MLP_holdout_metrics, MLP_holdout_history = train_model(
MLP,
x_train.values,
x_test.values,
y_train.values,
y_test.values,
epochs,
batch_size
)
MLP_tuned_metrics[window_type].append(MLP_holdout_metrics)
MLP_tuned_history[window_type].append(MLP_holdout_history)
Trial 8 Complete [00h 00m 25s]
multi_objective: -2.7589837312698364
Best multi_objective So Far: -2.7711485028266907
Total elapsed time: 00h 04m 12s
INFO:tensorflow:Oracle triggered exit
-- Best set of hyperparameters found:
{'depth': 3, 'units_0': 104, 'dropout_0': 0.3, 'units_1': 216, 'dropout_1': 0.4, 'units_2': 120, 'dropout_2': 0.3, 'units_3': 104, 'dropout_3': 0.5, 'units_4': 40, 'dropout_4': 0.5}
-- 6 Uncorrelated features: [Pearson+Spearman]
['mfcc16_mean', 'tempo', 'mfcc10_var', 'mfcc19_mean', 'mfcc11_var', 'mfcc17_mean']
-- Actually uncorrelated features: [confirmed via MIC]
[]
-- 1 Correlated features: [Pearson]
['spectral_centroid_mean']
Model: "MLP"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_2 (Dense) (None, 104) 5928
dropout_1 (Dropout) (None, 104) 0
dense_3 (Dense) (None, 216) 22680
dropout_2 (Dropout) (None, 216) 0
dense_4 (Dense) (None, 120) 26040
dropout_3 (Dropout) (None, 120) 0
dense_5 (Dense) (None, 10) 1210
=================================================================
Total params: 55,858
Trainable params: 55,858
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 2s 17ms/step - loss: 2.1267 - accuracy: 0.2632 - recall: 0.0561 - precision: 0.5443 - AUROC: 0.7112 - AUPRC: 0.2459 - f1_score: 0.1017 - balanced_accuracy: 0.5254 - specificity: 0.9948 - miss_rate: 0.9439 - fall_out: 0.0052 - mcc: 0.1512 - val_loss: 1.6159 - val_accuracy: 0.4133 - val_recall: 0.1884 - val_precision: 0.7721 - val_AUROC: 0.8579 - val_AUPRC: 0.4763 - val_f1_score: 0.3029 - val_balanced_accuracy: 0.5911 - val_specificity: 0.9938 - val_miss_rate: 0.8116 - val_fall_out: 0.0062 - val_mcc: 0.3543
Epoch 2/100
63/63 [==============================] - 1s 9ms/step - loss: 1.6552 - accuracy: 0.4155 - recall: 0.1842 - precision: 0.6729 - AUROC: 0.8440 - AUPRC: 0.4337 - f1_score: 0.2893 - balanced_accuracy: 0.5871 - specificity: 0.9900 - miss_rate: 0.8158 - fall_out: 0.0100 - mcc: 0.3204 - val_loss: 1.3270 - val_accuracy: 0.5416 - val_recall: 0.2761 - val_precision: 0.8091 - val_AUROC: 0.9089 - val_AUPRC: 0.6009 - val_f1_score: 0.4117 - val_balanced_accuracy: 0.6344 - val_specificity: 0.9928 - val_miss_rate: 0.7239 - val_fall_out: 0.0072 - val_mcc: 0.4442
Epoch 3/100
63/63 [==============================] - 1s 8ms/step - loss: 1.4392 - accuracy: 0.5009 - recall: 0.2751 - precision: 0.7050 - AUROC: 0.8852 - AUPRC: 0.5322 - f1_score: 0.3957 - balanced_accuracy: 0.6311 - specificity: 0.9872 - miss_rate: 0.7249 - fall_out: 0.0128 - mcc: 0.4063 - val_loss: 1.1502 - val_accuracy: 0.6288 - val_recall: 0.3572 - val_precision: 0.8224 - val_AUROC: 0.9306 - val_AUPRC: 0.6769 - val_f1_score: 0.4981 - val_balanced_accuracy: 0.6743 - val_specificity: 0.9914 - val_miss_rate: 0.6428 - val_fall_out: 0.0086 - val_mcc: 0.5131
Epoch 4/100
63/63 [==============================] - 1s 8ms/step - loss: 1.2845 - accuracy: 0.5524 - recall: 0.3498 - precision: 0.7184 - AUROC: 0.9096 - AUPRC: 0.5966 - f1_score: 0.4705 - balanced_accuracy: 0.6673 - specificity: 0.9848 - miss_rate: 0.6502 - fall_out: 0.0152 - mcc: 0.4664 - val_loss: 1.0185 - val_accuracy: 0.6648 - val_recall: 0.4389 - val_precision: 0.8367 - val_AUROC: 0.9454 - val_AUPRC: 0.7397 - val_f1_score: 0.5757 - val_balanced_accuracy: 0.7147 - val_specificity: 0.9905 - val_miss_rate: 0.5611 - val_fall_out: 0.0095 - val_mcc: 0.5778
Epoch 5/100
63/63 [==============================] - 1s 8ms/step - loss: 1.1811 - accuracy: 0.5932 - recall: 0.4094 - precision: 0.7386 - AUROC: 0.9224 - AUPRC: 0.6454 - f1_score: 0.5268 - balanced_accuracy: 0.6967 - specificity: 0.9839 - miss_rate: 0.5906 - fall_out: 0.0161 - mcc: 0.5157 - val_loss: 0.9493 - val_accuracy: 0.6889 - val_recall: 0.4845 - val_precision: 0.8490 - val_AUROC: 0.9524 - val_AUPRC: 0.7691 - val_f1_score: 0.6169 - val_balanced_accuracy: 0.7374 - val_specificity: 0.9904 - val_miss_rate: 0.5155 - val_fall_out: 0.0096 - val_mcc: 0.6142
Epoch 6/100
63/63 [==============================] - 1s 9ms/step - loss: 1.0954 - accuracy: 0.6092 - recall: 0.4502 - precision: 0.7582 - AUROC: 0.9331 - AUPRC: 0.6804 - f1_score: 0.5649 - balanced_accuracy: 0.7171 - specificity: 0.9841 - miss_rate: 0.5498 - fall_out: 0.0159 - mcc: 0.5512 - val_loss: 0.8802 - val_accuracy: 0.7074 - val_recall: 0.5481 - val_precision: 0.8402 - val_AUROC: 0.9577 - val_AUPRC: 0.7923 - val_f1_score: 0.6634 - val_balanced_accuracy: 0.7683 - val_specificity: 0.9884 - val_miss_rate: 0.4519 - val_fall_out: 0.0116 - val_mcc: 0.6518
Epoch 7/100
63/63 [==============================] - 1s 9ms/step - loss: 1.0551 - accuracy: 0.6345 - recall: 0.4788 - precision: 0.7657 - AUROC: 0.9375 - AUPRC: 0.6991 - f1_score: 0.5892 - balanced_accuracy: 0.7313 - specificity: 0.9837 - miss_rate: 0.5212 - fall_out: 0.0163 - mcc: 0.5731 - val_loss: 0.8570 - val_accuracy: 0.7224 - val_recall: 0.5581 - val_precision: 0.8523 - val_AUROC: 0.9593 - val_AUPRC: 0.7981 - val_f1_score: 0.6745 - val_balanced_accuracy: 0.7737 - val_specificity: 0.9893 - val_miss_rate: 0.4419 - val_fall_out: 0.0107 - val_mcc: 0.6638
Epoch 8/100
63/63 [==============================] - 1s 8ms/step - loss: 1.0011 - accuracy: 0.6586 - recall: 0.5091 - precision: 0.7808 - AUROC: 0.9430 - AUPRC: 0.7255 - f1_score: 0.6164 - balanced_accuracy: 0.7466 - specificity: 0.9841 - miss_rate: 0.4909 - fall_out: 0.0159 - mcc: 0.5994 - val_loss: 0.8058 - val_accuracy: 0.7335 - val_recall: 0.5927 - val_precision: 0.8566 - val_AUROC: 0.9647 - val_AUPRC: 0.8210 - val_f1_score: 0.7006 - val_balanced_accuracy: 0.7908 - val_specificity: 0.9890 - val_miss_rate: 0.4073 - val_fall_out: 0.0110 - val_mcc: 0.6876
Epoch 9/100
63/63 [==============================] - 1s 8ms/step - loss: 0.9560 - accuracy: 0.6750 - recall: 0.5403 - precision: 0.7812 - AUROC: 0.9478 - AUPRC: 0.7430 - f1_score: 0.6388 - balanced_accuracy: 0.7618 - specificity: 0.9832 - miss_rate: 0.4597 - fall_out: 0.0168 - mcc: 0.6190 - val_loss: 0.7716 - val_accuracy: 0.7480 - val_recall: 0.6117 - val_precision: 0.8641 - val_AUROC: 0.9671 - val_AUPRC: 0.8329 - val_f1_score: 0.7163 - val_balanced_accuracy: 0.8005 - val_specificity: 0.9893 - val_miss_rate: 0.3883 - val_fall_out: 0.0107 - val_mcc: 0.7030
Epoch 10/100
63/63 [==============================] - 1s 8ms/step - loss: 0.9378 - accuracy: 0.6894 - recall: 0.5506 - precision: 0.7906 - AUROC: 0.9498 - AUPRC: 0.7526 - f1_score: 0.6491 - balanced_accuracy: 0.7672 - specificity: 0.9838 - miss_rate: 0.4494 - fall_out: 0.0162 - mcc: 0.6298 - val_loss: 0.7577 - val_accuracy: 0.7515 - val_recall: 0.6273 - val_precision: 0.8658 - val_AUROC: 0.9683 - val_AUPRC: 0.8371 - val_f1_score: 0.7275 - val_balanced_accuracy: 0.8082 - val_specificity: 0.9892 - val_miss_rate: 0.3727 - val_fall_out: 0.0108 - val_mcc: 0.7134
Epoch 11/100
63/63 [==============================] - 1s 9ms/step - loss: 0.8869 - accuracy: 0.6965 - recall: 0.5744 - precision: 0.8001 - AUROC: 0.9551 - AUPRC: 0.7721 - f1_score: 0.6687 - balanced_accuracy: 0.7792 - specificity: 0.9841 - miss_rate: 0.4256 - fall_out: 0.0159 - mcc: 0.6490 - val_loss: 0.7308 - val_accuracy: 0.7600 - val_recall: 0.6458 - val_precision: 0.8599 - val_AUROC: 0.9702 - val_AUPRC: 0.8452 - val_f1_score: 0.7376 - val_balanced_accuracy: 0.8171 - val_specificity: 0.9883 - val_miss_rate: 0.3542 - val_fall_out: 0.0117 - val_mcc: 0.7218
Epoch 12/100
63/63 [==============================] - 1s 8ms/step - loss: 0.8618 - accuracy: 0.7049 - recall: 0.5897 - precision: 0.8077 - AUROC: 0.9574 - AUPRC: 0.7841 - f1_score: 0.6817 - balanced_accuracy: 0.7870 - specificity: 0.9844 - miss_rate: 0.4103 - fall_out: 0.0156 - mcc: 0.6620 - val_loss: 0.6996 - val_accuracy: 0.7625 - val_recall: 0.6648 - val_precision: 0.8679 - val_AUROC: 0.9725 - val_AUPRC: 0.8554 - val_f1_score: 0.7529 - val_balanced_accuracy: 0.8268 - val_specificity: 0.9888 - val_miss_rate: 0.3352 - val_fall_out: 0.0112 - val_mcc: 0.7372
Epoch 13/100
63/63 [==============================] - 1s 9ms/step - loss: 0.8224 - accuracy: 0.7166 - recall: 0.6122 - precision: 0.8118 - AUROC: 0.9609 - AUPRC: 0.7982 - f1_score: 0.6980 - balanced_accuracy: 0.7982 - specificity: 0.9842 - miss_rate: 0.3878 - fall_out: 0.0158 - mcc: 0.6776 - val_loss: 0.6763 - val_accuracy: 0.7740 - val_recall: 0.6824 - val_precision: 0.8692 - val_AUROC: 0.9744 - val_AUPRC: 0.8621 - val_f1_score: 0.7645 - val_balanced_accuracy: 0.8355 - val_specificity: 0.9886 - val_miss_rate: 0.3176 - val_fall_out: 0.0114 - val_mcc: 0.7484
Epoch 14/100
63/63 [==============================] - 1s 8ms/step - loss: 0.8085 - accuracy: 0.7246 - recall: 0.6206 - precision: 0.8111 - AUROC: 0.9620 - AUPRC: 0.8047 - f1_score: 0.7032 - balanced_accuracy: 0.8023 - specificity: 0.9839 - miss_rate: 0.3794 - fall_out: 0.0161 - mcc: 0.6823 - val_loss: 0.6678 - val_accuracy: 0.7745 - val_recall: 0.6814 - val_precision: 0.8696 - val_AUROC: 0.9752 - val_AUPRC: 0.8664 - val_f1_score: 0.7640 - val_balanced_accuracy: 0.8350 - val_specificity: 0.9886 - val_miss_rate: 0.3186 - val_fall_out: 0.0114 - val_mcc: 0.7480
Epoch 15/100
63/63 [==============================] - 1s 8ms/step - loss: 0.7892 - accuracy: 0.7373 - recall: 0.6366 - precision: 0.8269 - AUROC: 0.9637 - AUPRC: 0.8138 - f1_score: 0.7194 - balanced_accuracy: 0.8109 - specificity: 0.9852 - miss_rate: 0.3634 - fall_out: 0.0148 - mcc: 0.6998 - val_loss: 0.6426 - val_accuracy: 0.7856 - val_recall: 0.7014 - val_precision: 0.8669 - val_AUROC: 0.9765 - val_AUPRC: 0.8735 - val_f1_score: 0.7754 - val_balanced_accuracy: 0.8447 - val_specificity: 0.9880 - val_miss_rate: 0.2986 - val_fall_out: 0.0120 - val_mcc: 0.7585
Epoch 16/100
63/63 [==============================] - 1s 8ms/step - loss: 0.7608 - accuracy: 0.7405 - recall: 0.6454 - precision: 0.8198 - AUROC: 0.9659 - AUPRC: 0.8201 - f1_score: 0.7222 - balanced_accuracy: 0.8148 - specificity: 0.9842 - miss_rate: 0.3546 - fall_out: 0.0158 - mcc: 0.7014 - val_loss: 0.6337 - val_accuracy: 0.7891 - val_recall: 0.7024 - val_precision: 0.8703 - val_AUROC: 0.9774 - val_AUPRC: 0.8773 - val_f1_score: 0.7774 - val_balanced_accuracy: 0.8454 - val_specificity: 0.9884 - val_miss_rate: 0.2976 - val_fall_out: 0.0116 - val_mcc: 0.7608
Epoch 17/100
63/63 [==============================] - 1s 8ms/step - loss: 0.7384 - accuracy: 0.7446 - recall: 0.6566 - precision: 0.8289 - AUROC: 0.9679 - AUPRC: 0.8311 - f1_score: 0.7327 - balanced_accuracy: 0.8208 - specificity: 0.9849 - miss_rate: 0.3434 - fall_out: 0.0151 - mcc: 0.7126 - val_loss: 0.6214 - val_accuracy: 0.7916 - val_recall: 0.7084 - val_precision: 0.8712 - val_AUROC: 0.9778 - val_AUPRC: 0.8807 - val_f1_score: 0.7814 - val_balanced_accuracy: 0.8484 - val_specificity: 0.9884 - val_miss_rate: 0.2916 - val_fall_out: 0.0116 - val_mcc: 0.7648
Epoch 18/100
63/63 [==============================] - 1s 8ms/step - loss: 0.7313 - accuracy: 0.7503 - recall: 0.6576 - precision: 0.8264 - AUROC: 0.9691 - AUPRC: 0.8328 - f1_score: 0.7324 - balanced_accuracy: 0.8211 - specificity: 0.9846 - miss_rate: 0.3424 - fall_out: 0.0154 - mcc: 0.7119 - val_loss: 0.6070 - val_accuracy: 0.7936 - val_recall: 0.7244 - val_precision: 0.8769 - val_AUROC: 0.9784 - val_AUPRC: 0.8848 - val_f1_score: 0.7934 - val_balanced_accuracy: 0.8566 - val_specificity: 0.9887 - val_miss_rate: 0.2756 - val_fall_out: 0.0113 - val_mcc: 0.7771
Epoch 19/100
63/63 [==============================] - 1s 8ms/step - loss: 0.7027 - accuracy: 0.7523 - recall: 0.6742 - precision: 0.8285 - AUROC: 0.9710 - AUPRC: 0.8427 - f1_score: 0.7435 - balanced_accuracy: 0.8294 - specificity: 0.9845 - miss_rate: 0.3258 - fall_out: 0.0155 - mcc: 0.7228 - val_loss: 0.6011 - val_accuracy: 0.7951 - val_recall: 0.7310 - val_precision: 0.8779 - val_AUROC: 0.9793 - val_AUPRC: 0.8860 - val_f1_score: 0.7977 - val_balanced_accuracy: 0.8598 - val_specificity: 0.9887 - val_miss_rate: 0.2690 - val_fall_out: 0.0113 - val_mcc: 0.7814
Epoch 20/100
63/63 [==============================] - 1s 9ms/step - loss: 0.6875 - accuracy: 0.7613 - recall: 0.6826 - precision: 0.8369 - AUROC: 0.9722 - AUPRC: 0.8488 - f1_score: 0.7519 - balanced_accuracy: 0.8339 - specificity: 0.9852 - miss_rate: 0.3174 - fall_out: 0.0148 - mcc: 0.7320 - val_loss: 0.5781 - val_accuracy: 0.7971 - val_recall: 0.7425 - val_precision: 0.8769 - val_AUROC: 0.9804 - val_AUPRC: 0.8931 - val_f1_score: 0.8041 - val_balanced_accuracy: 0.8655 - val_specificity: 0.9884 - val_miss_rate: 0.2575 - val_fall_out: 0.0116 - val_mcc: 0.7876
Epoch 21/100
63/63 [==============================] - 1s 8ms/step - loss: 0.6838 - accuracy: 0.7682 - recall: 0.6888 - precision: 0.8344 - AUROC: 0.9723 - AUPRC: 0.8489 - f1_score: 0.7546 - balanced_accuracy: 0.8368 - specificity: 0.9848 - miss_rate: 0.3112 - fall_out: 0.0152 - mcc: 0.7343 - val_loss: 0.5760 - val_accuracy: 0.8061 - val_recall: 0.7375 - val_precision: 0.8778 - val_AUROC: 0.9807 - val_AUPRC: 0.8951 - val_f1_score: 0.8015 - val_balanced_accuracy: 0.8630 - val_specificity: 0.9886 - val_miss_rate: 0.2625 - val_fall_out: 0.0114 - val_mcc: 0.7852
Epoch 22/100
63/63 [==============================] - 1s 9ms/step - loss: 0.6595 - accuracy: 0.7730 - recall: 0.6939 - precision: 0.8435 - AUROC: 0.9744 - AUPRC: 0.8580 - f1_score: 0.7614 - balanced_accuracy: 0.8398 - specificity: 0.9857 - miss_rate: 0.3061 - fall_out: 0.0143 - mcc: 0.7420 - val_loss: 0.5576 - val_accuracy: 0.8141 - val_recall: 0.7495 - val_precision: 0.8774 - val_AUROC: 0.9813 - val_AUPRC: 0.8992 - val_f1_score: 0.8084 - val_balanced_accuracy: 0.8689 - val_specificity: 0.9884 - val_miss_rate: 0.2505 - val_fall_out: 0.0116 - val_mcc: 0.7920
Epoch 23/100
63/63 [==============================] - 1s 9ms/step - loss: 0.6403 - accuracy: 0.7804 - recall: 0.7128 - precision: 0.8437 - AUROC: 0.9753 - AUPRC: 0.8640 - f1_score: 0.7728 - balanced_accuracy: 0.8491 - specificity: 0.9853 - miss_rate: 0.2872 - fall_out: 0.0147 - mcc: 0.7531 - val_loss: 0.5607 - val_accuracy: 0.8121 - val_recall: 0.7470 - val_precision: 0.8791 - val_AUROC: 0.9814 - val_AUPRC: 0.8989 - val_f1_score: 0.8077 - val_balanced_accuracy: 0.8678 - val_specificity: 0.9886 - val_miss_rate: 0.2530 - val_fall_out: 0.0114 - val_mcc: 0.7914
Epoch 24/100
63/63 [==============================] - 1s 9ms/step - loss: 0.6414 - accuracy: 0.7891 - recall: 0.7174 - precision: 0.8461 - AUROC: 0.9750 - AUPRC: 0.8633 - f1_score: 0.7765 - balanced_accuracy: 0.8515 - specificity: 0.9855 - miss_rate: 0.2826 - fall_out: 0.0145 - mcc: 0.7570 - val_loss: 0.5448 - val_accuracy: 0.8171 - val_recall: 0.7555 - val_precision: 0.8793 - val_AUROC: 0.9826 - val_AUPRC: 0.9030 - val_f1_score: 0.8127 - val_balanced_accuracy: 0.8720 - val_specificity: 0.9885 - val_miss_rate: 0.2445 - val_fall_out: 0.0115 - val_mcc: 0.7964
Epoch 25/100
63/63 [==============================] - 1s 9ms/step - loss: 0.6202 - accuracy: 0.7873 - recall: 0.7229 - precision: 0.8510 - AUROC: 0.9768 - AUPRC: 0.8715 - f1_score: 0.7817 - balanced_accuracy: 0.8544 - specificity: 0.9859 - miss_rate: 0.2771 - fall_out: 0.0141 - mcc: 0.7627 - val_loss: 0.5372 - val_accuracy: 0.8176 - val_recall: 0.7635 - val_precision: 0.8769 - val_AUROC: 0.9827 - val_AUPRC: 0.9048 - val_f1_score: 0.8163 - val_balanced_accuracy: 0.8758 - val_specificity: 0.9881 - val_miss_rate: 0.2365 - val_fall_out: 0.0119 - val_mcc: 0.7997
Epoch 26/100
63/63 [==============================] - 1s 9ms/step - loss: 0.6113 - accuracy: 0.7913 - recall: 0.7263 - precision: 0.8528 - AUROC: 0.9776 - AUPRC: 0.8747 - f1_score: 0.7845 - balanced_accuracy: 0.8562 - specificity: 0.9861 - miss_rate: 0.2737 - fall_out: 0.0139 - mcc: 0.7656 - val_loss: 0.5301 - val_accuracy: 0.8166 - val_recall: 0.7695 - val_precision: 0.8807 - val_AUROC: 0.9831 - val_AUPRC: 0.9080 - val_f1_score: 0.8214 - val_balanced_accuracy: 0.8790 - val_specificity: 0.9884 - val_miss_rate: 0.2305 - val_fall_out: 0.0116 - val_mcc: 0.8052
Epoch 27/100
63/63 [==============================] - 1s 9ms/step - loss: 0.6202 - accuracy: 0.7922 - recall: 0.7311 - precision: 0.8501 - AUROC: 0.9766 - AUPRC: 0.8705 - f1_score: 0.7861 - balanced_accuracy: 0.8584 - specificity: 0.9857 - miss_rate: 0.2689 - fall_out: 0.0143 - mcc: 0.7670 - val_loss: 0.5239 - val_accuracy: 0.8236 - val_recall: 0.7705 - val_precision: 0.8875 - val_AUROC: 0.9836 - val_AUPRC: 0.9109 - val_f1_score: 0.8249 - val_balanced_accuracy: 0.8798 - val_specificity: 0.9891 - val_miss_rate: 0.2295 - val_fall_out: 0.0109 - val_mcc: 0.8094
Epoch 28/100
63/63 [==============================] - 1s 9ms/step - loss: 0.5844 - accuracy: 0.7976 - recall: 0.7363 - precision: 0.8550 - AUROC: 0.9795 - AUPRC: 0.8830 - f1_score: 0.7913 - balanced_accuracy: 0.8612 - specificity: 0.9861 - miss_rate: 0.2637 - fall_out: 0.0139 - mcc: 0.7726 - val_loss: 0.5159 - val_accuracy: 0.8257 - val_recall: 0.7796 - val_precision: 0.8891 - val_AUROC: 0.9839 - val_AUPRC: 0.9116 - val_f1_score: 0.8308 - val_balanced_accuracy: 0.8844 - val_specificity: 0.9892 - val_miss_rate: 0.2204 - val_fall_out: 0.0108 - val_mcc: 0.8155
Epoch 29/100
63/63 [==============================] - 1s 9ms/step - loss: 0.5736 - accuracy: 0.8050 - recall: 0.7460 - precision: 0.8601 - AUROC: 0.9803 - AUPRC: 0.8877 - f1_score: 0.7990 - balanced_accuracy: 0.8663 - specificity: 0.9865 - miss_rate: 0.2540 - fall_out: 0.0135 - mcc: 0.7808 - val_loss: 0.5011 - val_accuracy: 0.8322 - val_recall: 0.7826 - val_precision: 0.8870 - val_AUROC: 0.9848 - val_AUPRC: 0.9151 - val_f1_score: 0.8315 - val_balanced_accuracy: 0.8857 - val_specificity: 0.9889 - val_miss_rate: 0.2174 - val_fall_out: 0.0111 - val_mcc: 0.8160
Epoch 30/100
63/63 [==============================] - 1s 9ms/step - loss: 0.5722 - accuracy: 0.8021 - recall: 0.7442 - precision: 0.8547 - AUROC: 0.9800 - AUPRC: 0.8863 - f1_score: 0.7957 - balanced_accuracy: 0.8651 - specificity: 0.9859 - miss_rate: 0.2558 - fall_out: 0.0141 - mcc: 0.7769 - val_loss: 0.4965 - val_accuracy: 0.8347 - val_recall: 0.7831 - val_precision: 0.8896 - val_AUROC: 0.9846 - val_AUPRC: 0.9156 - val_f1_score: 0.8329 - val_balanced_accuracy: 0.8861 - val_specificity: 0.9892 - val_miss_rate: 0.2169 - val_fall_out: 0.0108 - val_mcc: 0.8177
Epoch 31/100
63/63 [==============================] - 1s 9ms/step - loss: 0.5439 - accuracy: 0.8110 - recall: 0.7594 - precision: 0.8636 - AUROC: 0.9816 - AUPRC: 0.8957 - f1_score: 0.8081 - balanced_accuracy: 0.8730 - specificity: 0.9867 - miss_rate: 0.2406 - fall_out: 0.0133 - mcc: 0.7903 - val_loss: 0.4878 - val_accuracy: 0.8382 - val_recall: 0.7881 - val_precision: 0.8907 - val_AUROC: 0.9854 - val_AUPRC: 0.9195 - val_f1_score: 0.8363 - val_balanced_accuracy: 0.8887 - val_specificity: 0.9893 - val_miss_rate: 0.2119 - val_fall_out: 0.0107 - val_mcc: 0.8212
Epoch 32/100
63/63 [==============================] - 1s 9ms/step - loss: 0.5325 - accuracy: 0.8188 - recall: 0.7678 - precision: 0.8656 - AUROC: 0.9826 - AUPRC: 0.9002 - f1_score: 0.8138 - balanced_accuracy: 0.8773 - specificity: 0.9868 - miss_rate: 0.2322 - fall_out: 0.0132 - mcc: 0.7962 - val_loss: 0.4831 - val_accuracy: 0.8347 - val_recall: 0.7911 - val_precision: 0.8886 - val_AUROC: 0.9851 - val_AUPRC: 0.9204 - val_f1_score: 0.8370 - val_balanced_accuracy: 0.8900 - val_specificity: 0.9890 - val_miss_rate: 0.2089 - val_fall_out: 0.0110 - val_mcc: 0.8217
Epoch 33/100
63/63 [==============================] - 1s 9ms/step - loss: 0.5456 - accuracy: 0.8129 - recall: 0.7640 - precision: 0.8616 - AUROC: 0.9818 - AUPRC: 0.8956 - f1_score: 0.8099 - balanced_accuracy: 0.8752 - specificity: 0.9864 - miss_rate: 0.2360 - fall_out: 0.0136 - mcc: 0.7919 - val_loss: 0.4778 - val_accuracy: 0.8392 - val_recall: 0.7926 - val_precision: 0.8928 - val_AUROC: 0.9858 - val_AUPRC: 0.9223 - val_f1_score: 0.8397 - val_balanced_accuracy: 0.8910 - val_specificity: 0.9894 - val_miss_rate: 0.2074 - val_fall_out: 0.0106 - val_mcc: 0.8248
Epoch 34/100
63/63 [==============================] - 1s 9ms/step - loss: 0.5245 - accuracy: 0.8190 - recall: 0.7650 - precision: 0.8721 - AUROC: 0.9834 - AUPRC: 0.9038 - f1_score: 0.8151 - balanced_accuracy: 0.8763 - specificity: 0.9875 - miss_rate: 0.2350 - fall_out: 0.0125 - mcc: 0.7981 - val_loss: 0.4708 - val_accuracy: 0.8452 - val_recall: 0.7976 - val_precision: 0.8894 - val_AUROC: 0.9864 - val_AUPRC: 0.9238 - val_f1_score: 0.8410 - val_balanced_accuracy: 0.8933 - val_specificity: 0.9890 - val_miss_rate: 0.2024 - val_fall_out: 0.0110 - val_mcc: 0.8259
Epoch 35/100
63/63 [==============================] - 1s 9ms/step - loss: 0.5254 - accuracy: 0.8194 - recall: 0.7695 - precision: 0.8683 - AUROC: 0.9831 - AUPRC: 0.9015 - f1_score: 0.8159 - balanced_accuracy: 0.8783 - specificity: 0.9870 - miss_rate: 0.2305 - fall_out: 0.0130 - mcc: 0.7986 - val_loss: 0.4762 - val_accuracy: 0.8427 - val_recall: 0.7966 - val_precision: 0.8888 - val_AUROC: 0.9859 - val_AUPRC: 0.9216 - val_f1_score: 0.8402 - val_balanced_accuracy: 0.8928 - val_specificity: 0.9889 - val_miss_rate: 0.2034 - val_fall_out: 0.0111 - val_mcc: 0.8250
Epoch 36/100
63/63 [==============================] - 1s 9ms/step - loss: 0.5149 - accuracy: 0.8240 - recall: 0.7763 - precision: 0.8700 - AUROC: 0.9836 - AUPRC: 0.9057 - f1_score: 0.8205 - balanced_accuracy: 0.8817 - specificity: 0.9871 - miss_rate: 0.2237 - fall_out: 0.0129 - mcc: 0.8034 - val_loss: 0.4627 - val_accuracy: 0.8427 - val_recall: 0.7991 - val_precision: 0.8941 - val_AUROC: 0.9864 - val_AUPRC: 0.9252 - val_f1_score: 0.8439 - val_balanced_accuracy: 0.8943 - val_specificity: 0.9895 - val_miss_rate: 0.2009 - val_fall_out: 0.0105 - val_mcc: 0.8292
Epoch 37/100
63/63 [==============================] - 1s 9ms/step - loss: 0.4985 - accuracy: 0.8293 - recall: 0.7874 - precision: 0.8713 - AUROC: 0.9849 - AUPRC: 0.9097 - f1_score: 0.8272 - balanced_accuracy: 0.8873 - specificity: 0.9871 - miss_rate: 0.2126 - fall_out: 0.0129 - mcc: 0.8104 - val_loss: 0.4573 - val_accuracy: 0.8412 - val_recall: 0.7986 - val_precision: 0.8895 - val_AUROC: 0.9870 - val_AUPRC: 0.9266 - val_f1_score: 0.8416 - val_balanced_accuracy: 0.8938 - val_specificity: 0.9890 - val_miss_rate: 0.2014 - val_fall_out: 0.0110 - val_mcc: 0.8265
Epoch 38/100
63/63 [==============================] - 1s 9ms/step - loss: 0.4969 - accuracy: 0.8320 - recall: 0.7852 - precision: 0.8743 - AUROC: 0.9849 - AUPRC: 0.9101 - f1_score: 0.8274 - balanced_accuracy: 0.8863 - specificity: 0.9875 - miss_rate: 0.2148 - fall_out: 0.0125 - mcc: 0.8108 - val_loss: 0.4505 - val_accuracy: 0.8477 - val_recall: 0.8106 - val_precision: 0.8920 - val_AUROC: 0.9872 - val_AUPRC: 0.9283 - val_f1_score: 0.8493 - val_balanced_accuracy: 0.8999 - val_specificity: 0.9891 - val_miss_rate: 0.1894 - val_fall_out: 0.0109 - val_mcc: 0.8347
Epoch 39/100
63/63 [==============================] - 1s 9ms/step - loss: 0.4779 - accuracy: 0.8412 - recall: 0.7965 - precision: 0.8820 - AUROC: 0.9858 - AUPRC: 0.9162 - f1_score: 0.8370 - balanced_accuracy: 0.8923 - specificity: 0.9882 - miss_rate: 0.2035 - fall_out: 0.0118 - mcc: 0.8213 - val_loss: 0.4466 - val_accuracy: 0.8487 - val_recall: 0.8091 - val_precision: 0.8918 - val_AUROC: 0.9872 - val_AUPRC: 0.9288 - val_f1_score: 0.8484 - val_balanced_accuracy: 0.8991 - val_specificity: 0.9891 - val_miss_rate: 0.1909 - val_fall_out: 0.0109 - val_mcc: 0.8337
Epoch 40/100
63/63 [==============================] - 1s 9ms/step - loss: 0.4772 - accuracy: 0.8365 - recall: 0.7977 - precision: 0.8796 - AUROC: 0.9858 - AUPRC: 0.9159 - f1_score: 0.8367 - balanced_accuracy: 0.8928 - specificity: 0.9879 - miss_rate: 0.2023 - fall_out: 0.0121 - mcc: 0.8207 - val_loss: 0.4511 - val_accuracy: 0.8457 - val_recall: 0.8151 - val_precision: 0.8891 - val_AUROC: 0.9872 - val_AUPRC: 0.9276 - val_f1_score: 0.8505 - val_balanced_accuracy: 0.9019 - val_specificity: 0.9887 - val_miss_rate: 0.1849 - val_fall_out: 0.0113 - val_mcc: 0.8356
Epoch 41/100
63/63 [==============================] - 1s 9ms/step - loss: 0.4720 - accuracy: 0.8419 - recall: 0.7992 - precision: 0.8848 - AUROC: 0.9857 - AUPRC: 0.9168 - f1_score: 0.8398 - balanced_accuracy: 0.8938 - specificity: 0.9884 - miss_rate: 0.2008 - fall_out: 0.0116 - mcc: 0.8243 - val_loss: 0.4401 - val_accuracy: 0.8527 - val_recall: 0.8156 - val_precision: 0.8950 - val_AUROC: 0.9878 - val_AUPRC: 0.9306 - val_f1_score: 0.8535 - val_balanced_accuracy: 0.9025 - val_specificity: 0.9894 - val_miss_rate: 0.1844 - val_fall_out: 0.0106 - val_mcc: 0.8391
Epoch 42/100
63/63 [==============================] - 1s 9ms/step - loss: 0.4397 - accuracy: 0.8464 - recall: 0.8089 - precision: 0.8879 - AUROC: 0.9877 - AUPRC: 0.9270 - f1_score: 0.8466 - balanced_accuracy: 0.8988 - specificity: 0.9887 - miss_rate: 0.1911 - fall_out: 0.0113 - mcc: 0.8315 - val_loss: 0.4362 - val_accuracy: 0.8517 - val_recall: 0.8302 - val_precision: 0.8942 - val_AUROC: 0.9874 - val_AUPRC: 0.9319 - val_f1_score: 0.8610 - val_balanced_accuracy: 0.9096 - val_specificity: 0.9891 - val_miss_rate: 0.1698 - val_fall_out: 0.0109 - val_mcc: 0.8469
Epoch 43/100
63/63 [==============================] - 1s 9ms/step - loss: 0.4643 - accuracy: 0.8439 - recall: 0.8091 - precision: 0.8813 - AUROC: 0.9862 - AUPRC: 0.9195 - f1_score: 0.8437 - balanced_accuracy: 0.8985 - specificity: 0.9879 - miss_rate: 0.1909 - fall_out: 0.0121 - mcc: 0.8280 - val_loss: 0.4267 - val_accuracy: 0.8542 - val_recall: 0.8231 - val_precision: 0.8963 - val_AUROC: 0.9880 - val_AUPRC: 0.9340 - val_f1_score: 0.8582 - val_balanced_accuracy: 0.9063 - val_specificity: 0.9894 - val_miss_rate: 0.1769 - val_fall_out: 0.0106 - val_mcc: 0.8441
Epoch 44/100
63/63 [==============================] - 1s 9ms/step - loss: 0.4614 - accuracy: 0.8427 - recall: 0.8007 - precision: 0.8822 - AUROC: 0.9870 - AUPRC: 0.9223 - f1_score: 0.8395 - balanced_accuracy: 0.8944 - specificity: 0.9881 - miss_rate: 0.1993 - fall_out: 0.0119 - mcc: 0.8238 - val_loss: 0.4395 - val_accuracy: 0.8527 - val_recall: 0.8201 - val_precision: 0.8906 - val_AUROC: 0.9873 - val_AUPRC: 0.9294 - val_f1_score: 0.8539 - val_balanced_accuracy: 0.9045 - val_specificity: 0.9888 - val_miss_rate: 0.1799 - val_fall_out: 0.0112 - val_mcc: 0.8393
Epoch 45/100
63/63 [==============================] - 1s 9ms/step - loss: 0.4307 - accuracy: 0.8484 - recall: 0.8146 - precision: 0.8842 - AUROC: 0.9883 - AUPRC: 0.9300 - f1_score: 0.8480 - balanced_accuracy: 0.9014 - specificity: 0.9881 - miss_rate: 0.1854 - fall_out: 0.0119 - mcc: 0.8327 - val_loss: 0.4266 - val_accuracy: 0.8487 - val_recall: 0.8216 - val_precision: 0.8928 - val_AUROC: 0.9882 - val_AUPRC: 0.9332 - val_f1_score: 0.8557 - val_balanced_accuracy: 0.9053 - val_specificity: 0.9890 - val_miss_rate: 0.1784 - val_fall_out: 0.0110 - val_mcc: 0.8413
Epoch 46/100
63/63 [==============================] - 1s 9ms/step - loss: 0.4448 - accuracy: 0.8463 - recall: 0.8089 - precision: 0.8820 - AUROC: 0.9874 - AUPRC: 0.9258 - f1_score: 0.8439 - balanced_accuracy: 0.8984 - specificity: 0.9880 - miss_rate: 0.1911 - fall_out: 0.0120 - mcc: 0.8283 - val_loss: 0.4250 - val_accuracy: 0.8562 - val_recall: 0.8257 - val_precision: 0.8991 - val_AUROC: 0.9881 - val_AUPRC: 0.9336 - val_f1_score: 0.8608 - val_balanced_accuracy: 0.9077 - val_specificity: 0.9897 - val_miss_rate: 0.1743 - val_fall_out: 0.0103 - val_mcc: 0.8470
Epoch 47/100
63/63 [==============================] - 1s 9ms/step - loss: 0.4324 - accuracy: 0.8488 - recall: 0.8140 - precision: 0.8871 - AUROC: 0.9881 - AUPRC: 0.9300 - f1_score: 0.8490 - balanced_accuracy: 0.9012 - specificity: 0.9885 - miss_rate: 0.1860 - fall_out: 0.0115 - mcc: 0.8339 - val_loss: 0.4163 - val_accuracy: 0.8642 - val_recall: 0.8327 - val_precision: 0.8984 - val_AUROC: 0.9890 - val_AUPRC: 0.9357 - val_f1_score: 0.8643 - val_balanced_accuracy: 0.9111 - val_specificity: 0.9895 - val_miss_rate: 0.1673 - val_fall_out: 0.0105 - val_mcc: 0.8506
Epoch 48/100
63/63 [==============================] - 1s 9ms/step - loss: 0.4288 - accuracy: 0.8571 - recall: 0.8208 - precision: 0.8918 - AUROC: 0.9880 - AUPRC: 0.9297 - f1_score: 0.8548 - balanced_accuracy: 0.9049 - specificity: 0.9889 - miss_rate: 0.1792 - fall_out: 0.0111 - mcc: 0.8403 - val_loss: 0.4082 - val_accuracy: 0.8642 - val_recall: 0.8287 - val_precision: 0.9053 - val_AUROC: 0.9888 - val_AUPRC: 0.9385 - val_f1_score: 0.8653 - val_balanced_accuracy: 0.9095 - val_specificity: 0.9904 - val_miss_rate: 0.1713 - val_fall_out: 0.0096 - val_mcc: 0.8521
Epoch 49/100
63/63 [==============================] - 1s 8ms/step - loss: 0.4171 - accuracy: 0.8617 - recall: 0.8283 - precision: 0.8958 - AUROC: 0.9884 - AUPRC: 0.9339 - f1_score: 0.8607 - balanced_accuracy: 0.9088 - specificity: 0.9893 - miss_rate: 0.1717 - fall_out: 0.0107 - mcc: 0.8467 - val_loss: 0.4111 - val_accuracy: 0.8647 - val_recall: 0.8337 - val_precision: 0.9004 - val_AUROC: 0.9881 - val_AUPRC: 0.9369 - val_f1_score: 0.8658 - val_balanced_accuracy: 0.9117 - val_specificity: 0.9898 - val_miss_rate: 0.1663 - val_fall_out: 0.0102 - val_mcc: 0.8523
Epoch 50/100
63/63 [==============================] - 1s 9ms/step - loss: 0.4197 - accuracy: 0.8616 - recall: 0.8280 - precision: 0.8946 - AUROC: 0.9883 - AUPRC: 0.9326 - f1_score: 0.8600 - balanced_accuracy: 0.9086 - specificity: 0.9892 - miss_rate: 0.1720 - fall_out: 0.0108 - mcc: 0.8459 - val_loss: 0.4108 - val_accuracy: 0.8602 - val_recall: 0.8302 - val_precision: 0.9010 - val_AUROC: 0.9883 - val_AUPRC: 0.9356 - val_f1_score: 0.8641 - val_balanced_accuracy: 0.9100 - val_specificity: 0.9899 - val_miss_rate: 0.1698 - val_fall_out: 0.0101 - val_mcc: 0.8506
250/250 [==============================] - 1s 4ms/step - loss: 0.1842 - accuracy: 0.9505 - recall: 0.9269 - precision: 0.9667 - AUROC: 0.9984 - AUPRC: 0.9880 - f1_score: 0.9464 - balanced_accuracy: 0.9617 - specificity: 0.9965 - miss_rate: 0.0731 - fall_out: 0.0035 - mcc: 0.9408
63/63 [==============================] - 0s 4ms/step - loss: 0.4108 - accuracy: 0.8602 - recall: 0.8302 - precision: 0.9010 - AUROC: 0.9884 - AUPRC: 0.9362 - f1_score: 0.8641 - balanced_accuracy: 0.9100 - specificity: 0.9899 - miss_rate: 0.1698 - fall_out: 0.0101 - mcc: 0.8506
10it [45:18, 271.84s/it]
for window_type in ("window_30s", "window_3s"):
MLP_metrics_estimate = model_metrics_holdout_estimate(MLP_tuned_metrics[window_type], number_of_splits)
print(f"-- WINDOW {window_type} --")
print(f"MLP Tuned Metrics - {number_of_splits}-holdouts estimate:")
print(f"Accuracy : train - {MLP_metrics_estimate['accuracy_train']} -- test - {MLP_metrics_estimate['accuracy_test']}")
print(f"AUROC : train - {MLP_metrics_estimate['AUROC_train']} -- test - {MLP_metrics_estimate['AUROC_test']}")
print(f"AUPRC : train - {MLP_metrics_estimate['AUPRC_train']} -- test - {MLP_metrics_estimate['AUPRC_test']}")
print("-"*80)
print("MLP - Train history:")
plot_train_history(MLP_tuned_history[window_type])
print("-"*100)
-- WINDOW window_30s -- MLP Tuned Metrics - 10-holdouts estimate: Accuracy : train - 0.8122653305530548 -- test - 0.6745000064373017 AUROC : train - 0.979555082321167 -- test - 0.9464241802692414 AUPRC : train - 0.8871137201786041 -- test - 0.7454391419887543 -------------------------------------------------------------------------------- MLP - Train history:
---------------------------------------------------------------------------------------------------- -- WINDOW window_3s -- MLP Tuned Metrics - 10-holdouts estimate: Accuracy : train - 0.9506262481212616 -- test - 0.8638276517391205 AUROC : train - 0.9981555879116059 -- test - 0.9882614970207214 AUPRC : train - 0.9877791702747345 -- test - 0.9361467361450195 -------------------------------------------------------------------------------- MLP - Train history:
----------------------------------------------------------------------------------------------------
It is literature consensus that mfccs are very powerful features for audio pattern recognition, in particular for this music genre classification task.
In order to determine to what extent other features are relevant and useful for the task the following models are tuned on the 30s and 3s window excluding mfccs mean and variance.
print("---- Tuned Other Features ----")
other_features = {}
other_features['features_30s'] = data['features_30s'].drop(data['features_3s'].loc[:, data['features_30s'].columns.str.startswith('mfcc')].columns, axis=1)
other_features['features_3s'] = data['features_3s'].drop(data['features_3s'].loc[:, data['features_3s'].columns.str.startswith('mfcc')].columns, axis=1)
feature_number = other_features['features_30s'].shape[1]
input_data = {"window_30s": other_features['features_30s'], "window_3s": other_features['features_3s']}
data_labels = {"window_30s": labels_30s, "window_3s": labels_3s}
MLP_other_features_metrics = {}
MLP_other_features_history = {}
MLP_other_features_metrics["window_30s"] = []
MLP_other_features_metrics["window_3s"] = []
MLP_other_features_history["window_30s"] = []
MLP_other_features_history["window_3s"] = []
for window_type in ("window_30s", "window_3s"):
# Generate holdouts
for holdout_number, (train_indices, test_indices) in enumerate(tqdm(holdouts_generator.split(input_data[window_type], data_labels[window_type]))):
print(f"-- HOLDOUT {holdout_number+1} -- WINDOW {window_type}")
# Train/Test data
x_train, x_test = input_data[window_type].iloc[train_indices], input_data[window_type].iloc[test_indices]
y_train, y_test = data_labels[window_type].iloc[train_indices], data_labels[window_type].iloc[test_indices]
## Hyperparameter tuning
# Generate holdouts
for holdout_number_tuning, (train_indices_tuning, val_indices_tuning) in enumerate(holdouts_generator_tuning.split(x_train, y_train)):
# Train/Validation data
x_train_tuning, x_val_tuning = x_train.iloc[train_indices_tuning], x_train.iloc[val_indices_tuning]
y_train_tuning, y_val_tuning = y_train.iloc[train_indices_tuning], y_train.iloc[val_indices_tuning]
# One-hot encoding
y_train_tuning = one_hot_encoding(y_train_tuning, 10)
y_val_tuning = one_hot_encoding(y_val_tuning, 10)
hp = kt.HyperParameters()
best_hyperparameters = hyperparameter_tuning(
x_train_tuning.values,
x_val_tuning.values,
y_train_tuning.values,
y_val_tuning.values,
build_MLP_hypermodel,
name = "MLP_hypermodel_no_mfccs_" + str(holdout_number) + "_" + str(window_type),
directory='MLP_hypermodel',
max_trials = 8,
epochs = 50,
batch_size = 128
)
## Remove uncorrelated features with the output
uncorrelated_features = uncorrelated_features_test(x_train, y_train)
for feature in (x_train.columns):
if feature in (uncorrelated_features):
x_train = x_train.drop(columns=feature)
x_test = x_test.drop(columns=feature)
## Remove correlated features with eachother
correlated_features = correlated_features_test(x_train)
for feature in (x_train.columns):
if feature in (correlated_features):
x_train = x_train.drop(columns=feature)
x_test = x_test.drop(columns=feature)
# One-hot encoding
y_train = one_hot_encoding(y_train, 10)
y_test = one_hot_encoding(y_test, 10)
# Build MLP with best set of hyperparameters
MLP = build_MLP(best_hyperparameters, x_train.shape)
print("- Training model:\n")
MLP_holdout_metrics, MLP_holdout_history = train_model(
MLP,
x_train.values,
x_test.values,
y_train.values,
y_test.values,
epochs,
batch_size
)
MLP_other_features_metrics[window_type].append(MLP_holdout_metrics)
MLP_other_features_history[window_type].append(MLP_holdout_history)
Trial 8 Complete [00h 00m 28s]
multi_objective: -1.9335779547691345
Best multi_objective So Far: -1.9346843957901
Total elapsed time: 00h 04m 14s
INFO:tensorflow:Oracle triggered exit
-- Best set of hyperparameters found:
{'depth': 1, 'units_0': 8, 'dropout_0': 0.3, 'units_1': 168, 'dropout_1': 0.5, 'units_2': 8, 'dropout_2': 0.3, 'units_3': 8, 'dropout_3': 0.3, 'units_4': 120, 'dropout_4': 0.5}
-- 1 Uncorrelated features: [Pearson+Spearman]
['tempo']
-- Actually uncorrelated features: [confirmed via MIC]
[]
-- 1 Correlated features: [Pearson]
['spectral_centroid_mean']
Model: "MLP"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_2 (Dense) (None, 8) 136
dropout_1 (Dropout) (None, 8) 0
dense_3 (Dense) (None, 10) 90
=================================================================
Total params: 226
Trainable params: 226
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 2s 14ms/step - loss: 3.2121 - accuracy: 0.1036 - recall: 0.0059 - precision: 0.0543 - AUROC: 0.5227 - AUPRC: 0.1044 - f1_score: 0.0106 - balanced_accuracy: 0.4972 - specificity: 0.9886 - miss_rate: 0.9941 - fall_out: 0.0114 - mcc: -0.0160 - val_loss: 2.8030 - val_accuracy: 0.1097 - val_recall: 0.0015 - val_precision: 0.0280 - val_AUROC: 0.5486 - val_AUPRC: 0.1111 - val_f1_score: 0.0029 - val_balanced_accuracy: 0.4979 - val_specificity: 0.9942 - val_miss_rate: 0.9985 - val_fall_out: 0.0058 - val_mcc: -0.0176
Epoch 2/100
63/63 [==============================] - 1s 9ms/step - loss: 2.6915 - accuracy: 0.1269 - recall: 0.0056 - precision: 0.1071 - AUROC: 0.5713 - AUPRC: 0.1221 - f1_score: 0.0107 - balanced_accuracy: 0.5002 - specificity: 0.9948 - miss_rate: 0.9944 - fall_out: 0.0052 - mcc: 0.0017 - val_loss: 2.4824 - val_accuracy: 0.1528 - val_recall: 0.0015 - val_precision: 0.0698 - val_AUROC: 0.6113 - val_AUPRC: 0.1405 - val_f1_score: 0.0029 - val_balanced_accuracy: 0.4996 - val_specificity: 0.9978 - val_miss_rate: 0.9985 - val_fall_out: 0.0022 - val_mcc: -0.0047
Epoch 3/100
63/63 [==============================] - 0s 7ms/step - loss: 2.4648 - accuracy: 0.1537 - recall: 0.0040 - precision: 0.1322 - AUROC: 0.6047 - AUPRC: 0.1435 - f1_score: 0.0078 - balanced_accuracy: 0.5005 - specificity: 0.9971 - miss_rate: 0.9960 - fall_out: 0.0029 - mcc: 0.0059 - val_loss: 2.3057 - val_accuracy: 0.1884 - val_recall: 0.0010 - val_precision: 0.0909 - val_AUROC: 0.6530 - val_AUPRC: 0.1748 - val_f1_score: 0.0020 - val_balanced_accuracy: 0.4999 - val_specificity: 0.9989 - val_miss_rate: 0.9990 - val_fall_out: 0.0011 - val_mcc: -0.0010
Epoch 4/100
63/63 [==============================] - 0s 6ms/step - loss: 2.3205 - accuracy: 0.1835 - recall: 0.0081 - precision: 0.3495 - AUROC: 0.6389 - AUPRC: 0.1737 - f1_score: 0.0159 - balanced_accuracy: 0.5032 - specificity: 0.9983 - miss_rate: 0.9919 - fall_out: 0.0017 - mcc: 0.0402 - val_loss: 2.1945 - val_accuracy: 0.2370 - val_recall: 0.0030 - val_precision: 0.3529 - val_AUROC: 0.6879 - val_AUPRC: 0.2146 - val_f1_score: 0.0060 - val_balanced_accuracy: 0.5012 - val_specificity: 0.9994 - val_miss_rate: 0.9970 - val_fall_out: 6.1234e-04 - val_mcc: 0.0246
Epoch 5/100
63/63 [==============================] - 0s 7ms/step - loss: 2.2179 - accuracy: 0.2087 - recall: 0.0120 - precision: 0.4873 - AUROC: 0.6638 - AUPRC: 0.1985 - f1_score: 0.0235 - balanced_accuracy: 0.5053 - specificity: 0.9986 - miss_rate: 0.9880 - fall_out: 0.0014 - mcc: 0.0642 - val_loss: 2.1078 - val_accuracy: 0.2806 - val_recall: 0.0055 - val_precision: 0.5789 - val_AUROC: 0.7179 - val_AUPRC: 0.2514 - val_f1_score: 0.0109 - val_balanced_accuracy: 0.5025 - val_specificity: 0.9996 - val_miss_rate: 0.9945 - val_fall_out: 4.4534e-04 - val_mcc: 0.0493
Epoch 6/100
63/63 [==============================] - 0s 7ms/step - loss: 2.1370 - accuracy: 0.2432 - recall: 0.0182 - precision: 0.6092 - AUROC: 0.6907 - AUPRC: 0.2290 - f1_score: 0.0353 - balanced_accuracy: 0.5084 - specificity: 0.9987 - miss_rate: 0.9818 - fall_out: 0.0013 - mcc: 0.0928 - val_loss: 2.0258 - val_accuracy: 0.3086 - val_recall: 0.0115 - val_precision: 0.6765 - val_AUROC: 0.7439 - val_AUPRC: 0.2913 - val_f1_score: 0.0227 - val_balanced_accuracy: 0.5055 - val_specificity: 0.9994 - val_miss_rate: 0.9885 - val_fall_out: 6.1234e-04 - val_mcc: 0.0794
Epoch 7/100
63/63 [==============================] - 0s 7ms/step - loss: 2.0682 - accuracy: 0.2665 - recall: 0.0307 - precision: 0.6005 - AUROC: 0.7128 - AUPRC: 0.2504 - f1_score: 0.0584 - balanced_accuracy: 0.5142 - specificity: 0.9977 - miss_rate: 0.9693 - fall_out: 0.0023 - mcc: 0.1196 - val_loss: 1.9536 - val_accuracy: 0.3277 - val_recall: 0.0185 - val_precision: 0.7551 - val_AUROC: 0.7693 - val_AUPRC: 0.3233 - val_f1_score: 0.0362 - val_balanced_accuracy: 0.5089 - val_specificity: 0.9993 - val_miss_rate: 0.9815 - val_fall_out: 6.6800e-04 - val_mcc: 0.1083
Epoch 8/100
63/63 [==============================] - 0s 7ms/step - loss: 1.9942 - accuracy: 0.2702 - recall: 0.0436 - precision: 0.6679 - AUROC: 0.7388 - AUPRC: 0.2734 - f1_score: 0.0818 - balanced_accuracy: 0.5206 - specificity: 0.9976 - miss_rate: 0.9564 - fall_out: 0.0024 - mcc: 0.1534 - val_loss: 1.8896 - val_accuracy: 0.3442 - val_recall: 0.0291 - val_precision: 0.7532 - val_AUROC: 0.7907 - val_AUPRC: 0.3445 - val_f1_score: 0.0560 - val_balanced_accuracy: 0.5140 - val_specificity: 0.9989 - val_miss_rate: 0.9709 - val_fall_out: 0.0011 - val_mcc: 0.1355
Epoch 9/100
63/63 [==============================] - 0s 7ms/step - loss: 1.9396 - accuracy: 0.2882 - recall: 0.0574 - precision: 0.6725 - AUROC: 0.7580 - AUPRC: 0.2977 - f1_score: 0.1057 - balanced_accuracy: 0.5271 - specificity: 0.9969 - miss_rate: 0.9426 - fall_out: 0.0031 - mcc: 0.1770 - val_loss: 1.8320 - val_accuracy: 0.3552 - val_recall: 0.0486 - val_precision: 0.7886 - val_AUROC: 0.8076 - val_AUPRC: 0.3623 - val_f1_score: 0.0916 - val_balanced_accuracy: 0.5236 - val_specificity: 0.9986 - val_miss_rate: 0.9514 - val_fall_out: 0.0014 - val_mcc: 0.1807
Epoch 10/100
63/63 [==============================] - 0s 7ms/step - loss: 1.8879 - accuracy: 0.3045 - recall: 0.0664 - precision: 0.6424 - AUROC: 0.7757 - AUPRC: 0.3156 - f1_score: 0.1203 - balanced_accuracy: 0.5311 - specificity: 0.9959 - miss_rate: 0.9336 - fall_out: 0.0041 - mcc: 0.1848 - val_loss: 1.7849 - val_accuracy: 0.3627 - val_recall: 0.0556 - val_precision: 0.7400 - val_AUROC: 0.8189 - val_AUPRC: 0.3770 - val_f1_score: 0.1034 - val_balanced_accuracy: 0.5267 - val_specificity: 0.9978 - val_miss_rate: 0.9444 - val_fall_out: 0.0022 - val_mcc: 0.1856
Epoch 11/100
63/63 [==============================] - 1s 9ms/step - loss: 1.8628 - accuracy: 0.3061 - recall: 0.0795 - precision: 0.6318 - AUROC: 0.7808 - AUPRC: 0.3244 - f1_score: 0.1413 - balanced_accuracy: 0.5372 - specificity: 0.9949 - miss_rate: 0.9205 - fall_out: 0.0051 - mcc: 0.2002 - val_loss: 1.7505 - val_accuracy: 0.3687 - val_recall: 0.0686 - val_precision: 0.7611 - val_AUROC: 0.8266 - val_AUPRC: 0.3867 - val_f1_score: 0.1259 - val_balanced_accuracy: 0.5331 - val_specificity: 0.9976 - val_miss_rate: 0.9314 - val_fall_out: 0.0024 - val_mcc: 0.2102
Epoch 12/100
63/63 [==============================] - 0s 7ms/step - loss: 1.8435 - accuracy: 0.3089 - recall: 0.0849 - precision: 0.6427 - AUROC: 0.7860 - AUPRC: 0.3281 - f1_score: 0.1500 - balanced_accuracy: 0.5398 - specificity: 0.9948 - miss_rate: 0.9151 - fall_out: 0.0052 - mcc: 0.2093 - val_loss: 1.7236 - val_accuracy: 0.3778 - val_recall: 0.0752 - val_precision: 0.7614 - val_AUROC: 0.8323 - val_AUPRC: 0.3988 - val_f1_score: 0.1368 - val_balanced_accuracy: 0.5363 - val_specificity: 0.9974 - val_miss_rate: 0.9248 - val_fall_out: 0.0026 - val_mcc: 0.2201
Epoch 13/100
63/63 [==============================] - 0s 8ms/step - loss: 1.8203 - accuracy: 0.3171 - recall: 0.0911 - precision: 0.6658 - AUROC: 0.7921 - AUPRC: 0.3403 - f1_score: 0.1602 - balanced_accuracy: 0.5430 - specificity: 0.9949 - miss_rate: 0.9089 - fall_out: 0.0051 - mcc: 0.2221 - val_loss: 1.7003 - val_accuracy: 0.3798 - val_recall: 0.0872 - val_precision: 0.7909 - val_AUROC: 0.8369 - val_AUPRC: 0.4091 - val_f1_score: 0.1570 - val_balanced_accuracy: 0.5423 - val_specificity: 0.9974 - val_miss_rate: 0.9128 - val_fall_out: 0.0026 - val_mcc: 0.2431
Epoch 14/100
63/63 [==============================] - 0s 7ms/step - loss: 1.7960 - accuracy: 0.3298 - recall: 0.0963 - precision: 0.6704 - AUROC: 0.7991 - AUPRC: 0.3494 - f1_score: 0.1684 - balanced_accuracy: 0.5455 - specificity: 0.9947 - miss_rate: 0.9037 - fall_out: 0.0053 - mcc: 0.2296 - val_loss: 1.6805 - val_accuracy: 0.3858 - val_recall: 0.0937 - val_precision: 0.7991 - val_AUROC: 0.8412 - val_AUPRC: 0.4190 - val_f1_score: 0.1677 - val_balanced_accuracy: 0.5455 - val_specificity: 0.9974 - val_miss_rate: 0.9063 - val_fall_out: 0.0026 - val_mcc: 0.2538
Epoch 15/100
63/63 [==============================] - 0s 7ms/step - loss: 1.7855 - accuracy: 0.3295 - recall: 0.0981 - precision: 0.6710 - AUROC: 0.8012 - AUPRC: 0.3551 - f1_score: 0.1711 - balanced_accuracy: 0.5464 - specificity: 0.9947 - miss_rate: 0.9019 - fall_out: 0.0053 - mcc: 0.2318 - val_loss: 1.6648 - val_accuracy: 0.3908 - val_recall: 0.1002 - val_precision: 0.7968 - val_AUROC: 0.8434 - val_AUPRC: 0.4237 - val_f1_score: 0.1780 - val_balanced_accuracy: 0.5487 - val_specificity: 0.9972 - val_miss_rate: 0.8998 - val_fall_out: 0.0028 - val_mcc: 0.2621
Epoch 16/100
63/63 [==============================] - 0s 7ms/step - loss: 1.7711 - accuracy: 0.3295 - recall: 0.1033 - precision: 0.6707 - AUROC: 0.8048 - AUPRC: 0.3571 - f1_score: 0.1791 - balanced_accuracy: 0.5488 - specificity: 0.9944 - miss_rate: 0.8967 - fall_out: 0.0056 - mcc: 0.2380 - val_loss: 1.6532 - val_accuracy: 0.3963 - val_recall: 0.1082 - val_precision: 0.8120 - val_AUROC: 0.8456 - val_AUPRC: 0.4292 - val_f1_score: 0.1910 - val_balanced_accuracy: 0.5527 - val_specificity: 0.9972 - val_miss_rate: 0.8918 - val_fall_out: 0.0028 - val_mcc: 0.2758
Epoch 17/100
63/63 [==============================] - 0s 7ms/step - loss: 1.7595 - accuracy: 0.3396 - recall: 0.1093 - precision: 0.6788 - AUROC: 0.8090 - AUPRC: 0.3668 - f1_score: 0.1883 - balanced_accuracy: 0.5518 - specificity: 0.9943 - miss_rate: 0.8907 - fall_out: 0.0057 - mcc: 0.2469 - val_loss: 1.6415 - val_accuracy: 0.4008 - val_recall: 0.1062 - val_precision: 0.8092 - val_AUROC: 0.8483 - val_AUPRC: 0.4345 - val_f1_score: 0.1878 - val_balanced_accuracy: 0.5517 - val_specificity: 0.9972 - val_miss_rate: 0.8938 - val_fall_out: 0.0028 - val_mcc: 0.2726
Epoch 18/100
63/63 [==============================] - 0s 7ms/step - loss: 1.7536 - accuracy: 0.3396 - recall: 0.1113 - precision: 0.6929 - AUROC: 0.8094 - AUPRC: 0.3664 - f1_score: 0.1919 - balanced_accuracy: 0.5529 - specificity: 0.9945 - miss_rate: 0.8887 - fall_out: 0.0055 - mcc: 0.2526 - val_loss: 1.6310 - val_accuracy: 0.3998 - val_recall: 0.1132 - val_precision: 0.8129 - val_AUROC: 0.8505 - val_AUPRC: 0.4407 - val_f1_score: 0.1988 - val_balanced_accuracy: 0.5552 - val_specificity: 0.9971 - val_miss_rate: 0.8868 - val_fall_out: 0.0029 - val_mcc: 0.2824
Epoch 19/100
63/63 [==============================] - 0s 7ms/step - loss: 1.7438 - accuracy: 0.3469 - recall: 0.1157 - precision: 0.6947 - AUROC: 0.8124 - AUPRC: 0.3737 - f1_score: 0.1984 - balanced_accuracy: 0.5550 - specificity: 0.9943 - miss_rate: 0.8843 - fall_out: 0.0057 - mcc: 0.2580 - val_loss: 1.6225 - val_accuracy: 0.4048 - val_recall: 0.1172 - val_precision: 0.8125 - val_AUROC: 0.8524 - val_AUPRC: 0.4446 - val_f1_score: 0.2049 - val_balanced_accuracy: 0.5571 - val_specificity: 0.9970 - val_miss_rate: 0.8828 - val_fall_out: 0.0030 - val_mcc: 0.2874
Epoch 20/100
63/63 [==============================] - 0s 7ms/step - loss: 1.7313 - accuracy: 0.3515 - recall: 0.1177 - precision: 0.6999 - AUROC: 0.8168 - AUPRC: 0.3781 - f1_score: 0.2016 - balanced_accuracy: 0.5561 - specificity: 0.9944 - miss_rate: 0.8823 - fall_out: 0.0056 - mcc: 0.2616 - val_loss: 1.6134 - val_accuracy: 0.4138 - val_recall: 0.1217 - val_precision: 0.8237 - val_AUROC: 0.8543 - val_AUPRC: 0.4503 - val_f1_score: 0.2121 - val_balanced_accuracy: 0.5594 - val_specificity: 0.9971 - val_miss_rate: 0.8783 - val_fall_out: 0.0029 - val_mcc: 0.2955
Epoch 21/100
63/63 [==============================] - 0s 7ms/step - loss: 1.7053 - accuracy: 0.3506 - recall: 0.1211 - precision: 0.7017 - AUROC: 0.8224 - AUPRC: 0.3853 - f1_score: 0.2066 - balanced_accuracy: 0.5577 - specificity: 0.9943 - miss_rate: 0.8789 - fall_out: 0.0057 - mcc: 0.2658 - val_loss: 1.6064 - val_accuracy: 0.4163 - val_recall: 0.1268 - val_precision: 0.8268 - val_AUROC: 0.8556 - val_AUPRC: 0.4551 - val_f1_score: 0.2198 - val_balanced_accuracy: 0.5619 - val_specificity: 0.9970 - val_miss_rate: 0.8732 - val_fall_out: 0.0030 - val_mcc: 0.3023
Epoch 22/100
63/63 [==============================] - 0s 7ms/step - loss: 1.7212 - accuracy: 0.3588 - recall: 0.1144 - precision: 0.6954 - AUROC: 0.8180 - AUPRC: 0.3816 - f1_score: 0.1964 - balanced_accuracy: 0.5544 - specificity: 0.9944 - miss_rate: 0.8856 - fall_out: 0.0056 - mcc: 0.2566 - val_loss: 1.5975 - val_accuracy: 0.4148 - val_recall: 0.1308 - val_precision: 0.8233 - val_AUROC: 0.8571 - val_AUPRC: 0.4588 - val_f1_score: 0.2257 - val_balanced_accuracy: 0.5638 - val_specificity: 0.9969 - val_miss_rate: 0.8692 - val_fall_out: 0.0031 - val_mcc: 0.3063
Epoch 23/100
63/63 [==============================] - 0s 7ms/step - loss: 1.7096 - accuracy: 0.3615 - recall: 0.1249 - precision: 0.7056 - AUROC: 0.8215 - AUPRC: 0.3870 - f1_score: 0.2122 - balanced_accuracy: 0.5595 - specificity: 0.9942 - miss_rate: 0.8751 - fall_out: 0.0058 - mcc: 0.2710 - val_loss: 1.5900 - val_accuracy: 0.4213 - val_recall: 0.1328 - val_precision: 0.8255 - val_AUROC: 0.8587 - val_AUPRC: 0.4616 - val_f1_score: 0.2287 - val_balanced_accuracy: 0.5648 - val_specificity: 0.9969 - val_miss_rate: 0.8672 - val_fall_out: 0.0031 - val_mcc: 0.3092
Epoch 24/100
63/63 [==============================] - 0s 7ms/step - loss: 1.7178 - accuracy: 0.3565 - recall: 0.1194 - precision: 0.6911 - AUROC: 0.8196 - AUPRC: 0.3851 - f1_score: 0.2036 - balanced_accuracy: 0.5567 - specificity: 0.9941 - miss_rate: 0.8806 - fall_out: 0.0059 - mcc: 0.2612 - val_loss: 1.5844 - val_accuracy: 0.4233 - val_recall: 0.1338 - val_precision: 0.8292 - val_AUROC: 0.8600 - val_AUPRC: 0.4654 - val_f1_score: 0.2304 - val_balanced_accuracy: 0.5654 - val_specificity: 0.9969 - val_miss_rate: 0.8662 - val_fall_out: 0.0031 - val_mcc: 0.3112
Epoch 25/100
63/63 [==============================] - 0s 7ms/step - loss: 1.6967 - accuracy: 0.3656 - recall: 0.1194 - precision: 0.6832 - AUROC: 0.8255 - AUPRC: 0.3929 - f1_score: 0.2032 - balanced_accuracy: 0.5566 - specificity: 0.9938 - miss_rate: 0.8806 - fall_out: 0.0062 - mcc: 0.2592 - val_loss: 1.5767 - val_accuracy: 0.4259 - val_recall: 0.1408 - val_precision: 0.8289 - val_AUROC: 0.8614 - val_AUPRC: 0.4683 - val_f1_score: 0.2407 - val_balanced_accuracy: 0.5688 - val_specificity: 0.9968 - val_miss_rate: 0.8592 - val_fall_out: 0.0032 - val_mcc: 0.3194
Epoch 26/100
63/63 [==============================] - 0s 7ms/step - loss: 1.6932 - accuracy: 0.3671 - recall: 0.1196 - precision: 0.6787 - AUROC: 0.8262 - AUPRC: 0.3932 - f1_score: 0.2034 - balanced_accuracy: 0.5567 - specificity: 0.9937 - miss_rate: 0.8804 - fall_out: 0.0063 - mcc: 0.2584 - val_loss: 1.5714 - val_accuracy: 0.4279 - val_recall: 0.1368 - val_precision: 0.8349 - val_AUROC: 0.8626 - val_AUPRC: 0.4713 - val_f1_score: 0.2350 - val_balanced_accuracy: 0.5669 - val_specificity: 0.9970 - val_miss_rate: 0.8632 - val_fall_out: 0.0030 - val_mcc: 0.3161
Epoch 27/100
63/63 [==============================] - 0s 7ms/step - loss: 1.6838 - accuracy: 0.3679 - recall: 0.1303 - precision: 0.7060 - AUROC: 0.8283 - AUPRC: 0.4005 - f1_score: 0.2199 - balanced_accuracy: 0.5621 - specificity: 0.9940 - miss_rate: 0.8697 - fall_out: 0.0060 - mcc: 0.2770 - val_loss: 1.5648 - val_accuracy: 0.4339 - val_recall: 0.1423 - val_precision: 0.8208 - val_AUROC: 0.8641 - val_AUPRC: 0.4739 - val_f1_score: 0.2425 - val_balanced_accuracy: 0.5694 - val_specificity: 0.9965 - val_miss_rate: 0.8577 - val_fall_out: 0.0035 - val_mcc: 0.3191
Epoch 28/100
63/63 [==============================] - 0s 7ms/step - loss: 1.6811 - accuracy: 0.3764 - recall: 0.1279 - precision: 0.6931 - AUROC: 0.8295 - AUPRC: 0.4028 - f1_score: 0.2159 - balanced_accuracy: 0.5608 - specificity: 0.9937 - miss_rate: 0.8721 - fall_out: 0.0063 - mcc: 0.2711 - val_loss: 1.5592 - val_accuracy: 0.4324 - val_recall: 0.1383 - val_precision: 0.8190 - val_AUROC: 0.8651 - val_AUPRC: 0.4782 - val_f1_score: 0.2366 - val_balanced_accuracy: 0.5674 - val_specificity: 0.9966 - val_miss_rate: 0.8617 - val_fall_out: 0.0034 - val_mcc: 0.3141
Epoch 29/100
63/63 [==============================] - 0s 7ms/step - loss: 1.6825 - accuracy: 0.3702 - recall: 0.1259 - precision: 0.6781 - AUROC: 0.8291 - AUPRC: 0.3966 - f1_score: 0.2123 - balanced_accuracy: 0.5596 - specificity: 0.9934 - miss_rate: 0.8741 - fall_out: 0.0066 - mcc: 0.2650 - val_loss: 1.5534 - val_accuracy: 0.4339 - val_recall: 0.1418 - val_precision: 0.8227 - val_AUROC: 0.8662 - val_AUPRC: 0.4804 - val_f1_score: 0.2419 - val_balanced_accuracy: 0.5692 - val_specificity: 0.9966 - val_miss_rate: 0.8582 - val_fall_out: 0.0034 - val_mcc: 0.3190
Epoch 30/100
63/63 [==============================] - 0s 7ms/step - loss: 1.6635 - accuracy: 0.3808 - recall: 0.1365 - precision: 0.7129 - AUROC: 0.8334 - AUPRC: 0.4105 - f1_score: 0.2292 - balanced_accuracy: 0.5652 - specificity: 0.9939 - miss_rate: 0.8635 - fall_out: 0.0061 - mcc: 0.2855 - val_loss: 1.5466 - val_accuracy: 0.4384 - val_recall: 0.1453 - val_precision: 0.8239 - val_AUROC: 0.8676 - val_AUPRC: 0.4829 - val_f1_score: 0.2470 - val_balanced_accuracy: 0.5709 - val_specificity: 0.9965 - val_miss_rate: 0.8547 - val_fall_out: 0.0035 - val_mcc: 0.3233
Epoch 31/100
63/63 [==============================] - 0s 7ms/step - loss: 1.6663 - accuracy: 0.3798 - recall: 0.1341 - precision: 0.7060 - AUROC: 0.8331 - AUPRC: 0.4091 - f1_score: 0.2254 - balanced_accuracy: 0.5640 - specificity: 0.9938 - miss_rate: 0.8659 - fall_out: 0.0062 - mcc: 0.2811 - val_loss: 1.5428 - val_accuracy: 0.4429 - val_recall: 0.1433 - val_precision: 0.8290 - val_AUROC: 0.8686 - val_AUPRC: 0.4857 - val_f1_score: 0.2443 - val_balanced_accuracy: 0.5700 - val_specificity: 0.9967 - val_miss_rate: 0.8567 - val_fall_out: 0.0033 - val_mcc: 0.3223
Epoch 32/100
63/63 [==============================] - 0s 7ms/step - loss: 1.6657 - accuracy: 0.3791 - recall: 0.1378 - precision: 0.6931 - AUROC: 0.8329 - AUPRC: 0.4088 - f1_score: 0.2299 - balanced_accuracy: 0.5655 - specificity: 0.9932 - miss_rate: 0.8622 - fall_out: 0.0068 - mcc: 0.2816 - val_loss: 1.5363 - val_accuracy: 0.4494 - val_recall: 0.1458 - val_precision: 0.8197 - val_AUROC: 0.8696 - val_AUPRC: 0.4879 - val_f1_score: 0.2476 - val_balanced_accuracy: 0.5711 - val_specificity: 0.9964 - val_miss_rate: 0.8542 - val_fall_out: 0.0036 - val_mcc: 0.3228
Epoch 33/100
63/63 [==============================] - 0s 7ms/step - loss: 1.6479 - accuracy: 0.3836 - recall: 0.1389 - precision: 0.6997 - AUROC: 0.8377 - AUPRC: 0.4155 - f1_score: 0.2318 - balanced_accuracy: 0.5661 - specificity: 0.9934 - miss_rate: 0.8611 - fall_out: 0.0066 - mcc: 0.2845 - val_loss: 1.5309 - val_accuracy: 0.4529 - val_recall: 0.1483 - val_precision: 0.8177 - val_AUROC: 0.8705 - val_AUPRC: 0.4909 - val_f1_score: 0.2511 - val_balanced_accuracy: 0.5723 - val_specificity: 0.9963 - val_miss_rate: 0.8517 - val_fall_out: 0.0037 - val_mcc: 0.3251
Epoch 34/100
63/63 [==============================] - 0s 7ms/step - loss: 1.6455 - accuracy: 0.3929 - recall: 0.1385 - precision: 0.7000 - AUROC: 0.8386 - AUPRC: 0.4174 - f1_score: 0.2313 - balanced_accuracy: 0.5660 - specificity: 0.9934 - miss_rate: 0.8615 - fall_out: 0.0066 - mcc: 0.2842 - val_loss: 1.5269 - val_accuracy: 0.4519 - val_recall: 0.1463 - val_precision: 0.8089 - val_AUROC: 0.8716 - val_AUPRC: 0.4933 - val_f1_score: 0.2478 - val_balanced_accuracy: 0.5712 - val_specificity: 0.9962 - val_miss_rate: 0.8537 - val_fall_out: 0.0038 - val_mcc: 0.3207
Epoch 35/100
63/63 [==============================] - 0s 7ms/step - loss: 1.6498 - accuracy: 0.3861 - recall: 0.1405 - precision: 0.7169 - AUROC: 0.8368 - AUPRC: 0.4192 - f1_score: 0.2350 - balanced_accuracy: 0.5672 - specificity: 0.9938 - miss_rate: 0.8595 - fall_out: 0.0062 - mcc: 0.2908 - val_loss: 1.5203 - val_accuracy: 0.4549 - val_recall: 0.1498 - val_precision: 0.8169 - val_AUROC: 0.8730 - val_AUPRC: 0.4977 - val_f1_score: 0.2532 - val_balanced_accuracy: 0.5730 - val_specificity: 0.9963 - val_miss_rate: 0.8502 - val_fall_out: 0.0037 - val_mcc: 0.3266
Epoch 36/100
63/63 [==============================] - 0s 7ms/step - loss: 1.6492 - accuracy: 0.3952 - recall: 0.1394 - precision: 0.7085 - AUROC: 0.8377 - AUPRC: 0.4139 - f1_score: 0.2330 - balanced_accuracy: 0.5665 - specificity: 0.9936 - miss_rate: 0.8606 - fall_out: 0.0064 - mcc: 0.2873 - val_loss: 1.5168 - val_accuracy: 0.4604 - val_recall: 0.1523 - val_precision: 0.8128 - val_AUROC: 0.8737 - val_AUPRC: 0.4992 - val_f1_score: 0.2565 - val_balanced_accuracy: 0.5742 - val_specificity: 0.9961 - val_miss_rate: 0.8477 - val_fall_out: 0.0039 - val_mcc: 0.3283
Epoch 37/100
63/63 [==============================] - 0s 7ms/step - loss: 1.6410 - accuracy: 0.4004 - recall: 0.1429 - precision: 0.7074 - AUROC: 0.8400 - AUPRC: 0.4190 - f1_score: 0.2378 - balanced_accuracy: 0.5682 - specificity: 0.9934 - miss_rate: 0.8571 - fall_out: 0.0066 - mcc: 0.2907 - val_loss: 1.5113 - val_accuracy: 0.4659 - val_recall: 0.1528 - val_precision: 0.8221 - val_AUROC: 0.8748 - val_AUPRC: 0.5022 - val_f1_score: 0.2577 - val_balanced_accuracy: 0.5746 - val_specificity: 0.9963 - val_miss_rate: 0.8472 - val_fall_out: 0.0037 - val_mcc: 0.3313
Epoch 38/100
63/63 [==============================] - 0s 7ms/step - loss: 1.6400 - accuracy: 0.3955 - recall: 0.1423 - precision: 0.7136 - AUROC: 0.8388 - AUPRC: 0.4200 - f1_score: 0.2373 - balanced_accuracy: 0.5680 - specificity: 0.9937 - miss_rate: 0.8577 - fall_out: 0.0063 - mcc: 0.2917 - val_loss: 1.5076 - val_accuracy: 0.4669 - val_recall: 0.1553 - val_precision: 0.8179 - val_AUROC: 0.8754 - val_AUPRC: 0.5037 - val_f1_score: 0.2611 - val_balanced_accuracy: 0.5757 - val_specificity: 0.9962 - val_miss_rate: 0.8447 - val_fall_out: 0.0038 - val_mcc: 0.3329
Epoch 39/100
63/63 [==============================] - 0s 7ms/step - loss: 1.6311 - accuracy: 0.4016 - recall: 0.1432 - precision: 0.7139 - AUROC: 0.8419 - AUPRC: 0.4245 - f1_score: 0.2385 - balanced_accuracy: 0.5684 - specificity: 0.9936 - miss_rate: 0.8568 - fall_out: 0.0064 - mcc: 0.2927 - val_loss: 1.5030 - val_accuracy: 0.4679 - val_recall: 0.1538 - val_precision: 0.8275 - val_AUROC: 0.8760 - val_AUPRC: 0.5070 - val_f1_score: 0.2594 - val_balanced_accuracy: 0.5751 - val_specificity: 0.9964 - val_miss_rate: 0.8462 - val_fall_out: 0.0036 - val_mcc: 0.3337
Epoch 40/100
63/63 [==============================] - 0s 7ms/step - loss: 1.6322 - accuracy: 0.4002 - recall: 0.1469 - precision: 0.7144 - AUROC: 0.8406 - AUPRC: 0.4291 - f1_score: 0.2437 - balanced_accuracy: 0.5702 - specificity: 0.9935 - miss_rate: 0.8531 - fall_out: 0.0065 - mcc: 0.2968 - val_loss: 1.4979 - val_accuracy: 0.4729 - val_recall: 0.1563 - val_precision: 0.8276 - val_AUROC: 0.8771 - val_AUPRC: 0.5095 - val_f1_score: 0.2630 - val_balanced_accuracy: 0.5763 - val_specificity: 0.9964 - val_miss_rate: 0.8437 - val_fall_out: 0.0036 - val_mcc: 0.3365
Epoch 41/100
63/63 [==============================] - 0s 7ms/step - loss: 1.6235 - accuracy: 0.4081 - recall: 0.1514 - precision: 0.7274 - AUROC: 0.8432 - AUPRC: 0.4312 - f1_score: 0.2507 - balanced_accuracy: 0.5726 - specificity: 0.9937 - miss_rate: 0.8486 - fall_out: 0.0063 - mcc: 0.3049 - val_loss: 1.4947 - val_accuracy: 0.4765 - val_recall: 0.1568 - val_precision: 0.8324 - val_AUROC: 0.8779 - val_AUPRC: 0.5120 - val_f1_score: 0.2639 - val_balanced_accuracy: 0.5767 - val_specificity: 0.9965 - val_miss_rate: 0.8432 - val_fall_out: 0.0035 - val_mcc: 0.3383
Epoch 42/100
63/63 [==============================] - 0s 7ms/step - loss: 1.6241 - accuracy: 0.4044 - recall: 0.1453 - precision: 0.7090 - AUROC: 0.8428 - AUPRC: 0.4287 - f1_score: 0.2412 - balanced_accuracy: 0.5693 - specificity: 0.9934 - miss_rate: 0.8547 - fall_out: 0.0066 - mcc: 0.2936 - val_loss: 1.4904 - val_accuracy: 0.4810 - val_recall: 0.1578 - val_precision: 0.8333 - val_AUROC: 0.8783 - val_AUPRC: 0.5138 - val_f1_score: 0.2654 - val_balanced_accuracy: 0.5772 - val_specificity: 0.9965 - val_miss_rate: 0.8422 - val_fall_out: 0.0035 - val_mcc: 0.3396
Epoch 43/100
63/63 [==============================] - 0s 7ms/step - loss: 1.6161 - accuracy: 0.4048 - recall: 0.1493 - precision: 0.7133 - AUROC: 0.8457 - AUPRC: 0.4330 - f1_score: 0.2469 - balanced_accuracy: 0.5713 - specificity: 0.9933 - miss_rate: 0.8507 - fall_out: 0.0067 - mcc: 0.2989 - val_loss: 1.4870 - val_accuracy: 0.4830 - val_recall: 0.1598 - val_precision: 0.8351 - val_AUROC: 0.8789 - val_AUPRC: 0.5158 - val_f1_score: 0.2683 - val_balanced_accuracy: 0.5782 - val_specificity: 0.9965 - val_miss_rate: 0.8402 - val_fall_out: 0.0035 - val_mcc: 0.3423
Epoch 44/100
63/63 [==============================] - 0s 7ms/step - loss: 1.6229 - accuracy: 0.4006 - recall: 0.1498 - precision: 0.7123 - AUROC: 0.8434 - AUPRC: 0.4299 - f1_score: 0.2475 - balanced_accuracy: 0.5715 - specificity: 0.9933 - miss_rate: 0.8502 - fall_out: 0.0067 - mcc: 0.2992 - val_loss: 1.4847 - val_accuracy: 0.4795 - val_recall: 0.1608 - val_precision: 0.8381 - val_AUROC: 0.8792 - val_AUPRC: 0.5163 - val_f1_score: 0.2699 - val_balanced_accuracy: 0.5787 - val_specificity: 0.9965 - val_miss_rate: 0.8392 - val_fall_out: 0.0035 - val_mcc: 0.3441
Epoch 45/100
63/63 [==============================] - 1s 8ms/step - loss: 1.6173 - accuracy: 0.4083 - recall: 0.1516 - precision: 0.7185 - AUROC: 0.8445 - AUPRC: 0.4331 - f1_score: 0.2503 - balanced_accuracy: 0.5725 - specificity: 0.9934 - miss_rate: 0.8484 - fall_out: 0.0066 - mcc: 0.3026 - val_loss: 1.4813 - val_accuracy: 0.4800 - val_recall: 0.1618 - val_precision: 0.8282 - val_AUROC: 0.8799 - val_AUPRC: 0.5180 - val_f1_score: 0.2707 - val_balanced_accuracy: 0.5790 - val_specificity: 0.9963 - val_miss_rate: 0.8382 - val_fall_out: 0.0037 - val_mcc: 0.3427
Epoch 46/100
63/63 [==============================] - 0s 6ms/step - loss: 1.6112 - accuracy: 0.4079 - recall: 0.1587 - precision: 0.7211 - AUROC: 0.8452 - AUPRC: 0.4393 - f1_score: 0.2601 - balanced_accuracy: 0.5759 - specificity: 0.9932 - miss_rate: 0.8413 - fall_out: 0.0068 - mcc: 0.3106 - val_loss: 1.4776 - val_accuracy: 0.4870 - val_recall: 0.1618 - val_precision: 0.8282 - val_AUROC: 0.8806 - val_AUPRC: 0.5195 - val_f1_score: 0.2707 - val_balanced_accuracy: 0.5790 - val_specificity: 0.9963 - val_miss_rate: 0.8382 - val_fall_out: 0.0037 - val_mcc: 0.3427
Epoch 47/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5989 - accuracy: 0.4136 - recall: 0.1593 - precision: 0.7122 - AUROC: 0.8487 - AUPRC: 0.4420 - f1_score: 0.2604 - balanced_accuracy: 0.5761 - specificity: 0.9928 - miss_rate: 0.8407 - fall_out: 0.0072 - mcc: 0.3087 - val_loss: 1.4723 - val_accuracy: 0.4840 - val_recall: 0.1643 - val_precision: 0.8241 - val_AUROC: 0.8812 - val_AUPRC: 0.5219 - val_f1_score: 0.2740 - val_balanced_accuracy: 0.5802 - val_specificity: 0.9961 - val_miss_rate: 0.8357 - val_fall_out: 0.0039 - val_mcc: 0.3443
Epoch 48/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5918 - accuracy: 0.4180 - recall: 0.1602 - precision: 0.7165 - AUROC: 0.8498 - AUPRC: 0.4441 - f1_score: 0.2618 - balanced_accuracy: 0.5766 - specificity: 0.9930 - miss_rate: 0.8398 - fall_out: 0.0070 - mcc: 0.3108 - val_loss: 1.4692 - val_accuracy: 0.4875 - val_recall: 0.1693 - val_precision: 0.8284 - val_AUROC: 0.8817 - val_AUPRC: 0.5233 - val_f1_score: 0.2812 - val_balanced_accuracy: 0.5827 - val_specificity: 0.9961 - val_miss_rate: 0.8307 - val_fall_out: 0.0039 - val_mcc: 0.3508
Epoch 49/100
63/63 [==============================] - 0s 7ms/step - loss: 1.6086 - accuracy: 0.4062 - recall: 0.1599 - precision: 0.7166 - AUROC: 0.8455 - AUPRC: 0.4392 - f1_score: 0.2615 - balanced_accuracy: 0.5765 - specificity: 0.9930 - miss_rate: 0.8401 - fall_out: 0.0070 - mcc: 0.3106 - val_loss: 1.4679 - val_accuracy: 0.4910 - val_recall: 0.1688 - val_precision: 0.8260 - val_AUROC: 0.8822 - val_AUPRC: 0.5249 - val_f1_score: 0.2804 - val_balanced_accuracy: 0.5824 - val_specificity: 0.9960 - val_miss_rate: 0.8312 - val_fall_out: 0.0040 - val_mcc: 0.3496
Epoch 50/100
63/63 [==============================] - 0s 8ms/step - loss: 1.5858 - accuracy: 0.4215 - recall: 0.1648 - precision: 0.7223 - AUROC: 0.8512 - AUPRC: 0.4509 - f1_score: 0.2684 - balanced_accuracy: 0.5789 - specificity: 0.9930 - miss_rate: 0.8352 - fall_out: 0.0070 - mcc: 0.3170 - val_loss: 1.4627 - val_accuracy: 0.4920 - val_recall: 0.1698 - val_precision: 0.8248 - val_AUROC: 0.8829 - val_AUPRC: 0.5263 - val_f1_score: 0.2817 - val_balanced_accuracy: 0.5829 - val_specificity: 0.9960 - val_miss_rate: 0.8302 - val_fall_out: 0.0040 - val_mcc: 0.3503
Epoch 51/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5923 - accuracy: 0.4180 - recall: 0.1638 - precision: 0.7109 - AUROC: 0.8508 - AUPRC: 0.4467 - f1_score: 0.2663 - balanced_accuracy: 0.5782 - specificity: 0.9926 - miss_rate: 0.8362 - fall_out: 0.0074 - mcc: 0.3127 - val_loss: 1.4618 - val_accuracy: 0.4940 - val_recall: 0.1673 - val_precision: 0.8227 - val_AUROC: 0.8832 - val_AUPRC: 0.5282 - val_f1_score: 0.2781 - val_balanced_accuracy: 0.5817 - val_specificity: 0.9960 - val_miss_rate: 0.8327 - val_fall_out: 0.0040 - val_mcc: 0.3471
Epoch 52/100
63/63 [==============================] - 0s 7ms/step - loss: 1.6051 - accuracy: 0.4143 - recall: 0.1601 - precision: 0.7120 - AUROC: 0.8473 - AUPRC: 0.4411 - f1_score: 0.2614 - balanced_accuracy: 0.5764 - specificity: 0.9928 - miss_rate: 0.8399 - fall_out: 0.0072 - mcc: 0.3094 - val_loss: 1.4583 - val_accuracy: 0.4925 - val_recall: 0.1703 - val_precision: 0.8252 - val_AUROC: 0.8838 - val_AUPRC: 0.5299 - val_f1_score: 0.2824 - val_balanced_accuracy: 0.5832 - val_specificity: 0.9960 - val_miss_rate: 0.8297 - val_fall_out: 0.0040 - val_mcc: 0.3510
Epoch 53/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5997 - accuracy: 0.4185 - recall: 0.1631 - precision: 0.7205 - AUROC: 0.8486 - AUPRC: 0.4432 - f1_score: 0.2660 - balanced_accuracy: 0.5780 - specificity: 0.9930 - miss_rate: 0.8369 - fall_out: 0.0070 - mcc: 0.3148 - val_loss: 1.4563 - val_accuracy: 0.4975 - val_recall: 0.1683 - val_precision: 0.8195 - val_AUROC: 0.8845 - val_AUPRC: 0.5319 - val_f1_score: 0.2793 - val_balanced_accuracy: 0.5821 - val_specificity: 0.9959 - val_miss_rate: 0.8317 - val_fall_out: 0.0041 - val_mcc: 0.3473
Epoch 54/100
63/63 [==============================] - 0s 8ms/step - loss: 1.5906 - accuracy: 0.4180 - recall: 0.1695 - precision: 0.7266 - AUROC: 0.8501 - AUPRC: 0.4518 - f1_score: 0.2748 - balanced_accuracy: 0.5812 - specificity: 0.9929 - miss_rate: 0.8305 - fall_out: 0.0071 - mcc: 0.3228 - val_loss: 1.4551 - val_accuracy: 0.4980 - val_recall: 0.1698 - val_precision: 0.8188 - val_AUROC: 0.8845 - val_AUPRC: 0.5317 - val_f1_score: 0.2813 - val_balanced_accuracy: 0.5828 - val_specificity: 0.9958 - val_miss_rate: 0.8302 - val_fall_out: 0.0042 - val_mcc: 0.3487
Epoch 55/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5902 - accuracy: 0.4201 - recall: 0.1683 - precision: 0.7281 - AUROC: 0.8501 - AUPRC: 0.4493 - f1_score: 0.2734 - balanced_accuracy: 0.5807 - specificity: 0.9930 - miss_rate: 0.8317 - fall_out: 0.0070 - mcc: 0.3221 - val_loss: 1.4522 - val_accuracy: 0.4945 - val_recall: 0.1728 - val_precision: 0.8214 - val_AUROC: 0.8847 - val_AUPRC: 0.5331 - val_f1_score: 0.2856 - val_balanced_accuracy: 0.5843 - val_specificity: 0.9958 - val_miss_rate: 0.8272 - val_fall_out: 0.0042 - val_mcc: 0.3526
Epoch 56/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5878 - accuracy: 0.4171 - recall: 0.1705 - precision: 0.7224 - AUROC: 0.8504 - AUPRC: 0.4502 - f1_score: 0.2758 - balanced_accuracy: 0.5816 - specificity: 0.9927 - miss_rate: 0.8295 - fall_out: 0.0073 - mcc: 0.3225 - val_loss: 1.4511 - val_accuracy: 0.4985 - val_recall: 0.1759 - val_precision: 0.8318 - val_AUROC: 0.8847 - val_AUPRC: 0.5334 - val_f1_score: 0.2903 - val_balanced_accuracy: 0.5859 - val_specificity: 0.9960 - val_miss_rate: 0.8241 - val_fall_out: 0.0040 - val_mcc: 0.3585
Epoch 57/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5763 - accuracy: 0.4202 - recall: 0.1754 - precision: 0.7353 - AUROC: 0.8531 - AUPRC: 0.4552 - f1_score: 0.2832 - balanced_accuracy: 0.5842 - specificity: 0.9930 - miss_rate: 0.8246 - fall_out: 0.0070 - mcc: 0.3310 - val_loss: 1.4498 - val_accuracy: 0.4985 - val_recall: 0.1723 - val_precision: 0.8152 - val_AUROC: 0.8850 - val_AUPRC: 0.5330 - val_f1_score: 0.2845 - val_balanced_accuracy: 0.5840 - val_specificity: 0.9957 - val_miss_rate: 0.8277 - val_fall_out: 0.0043 - val_mcc: 0.3503
Epoch 58/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5885 - accuracy: 0.4203 - recall: 0.1723 - precision: 0.7300 - AUROC: 0.8509 - AUPRC: 0.4528 - f1_score: 0.2789 - balanced_accuracy: 0.5826 - specificity: 0.9929 - miss_rate: 0.8277 - fall_out: 0.0071 - mcc: 0.3265 - val_loss: 1.4472 - val_accuracy: 0.5000 - val_recall: 0.1759 - val_precision: 0.8163 - val_AUROC: 0.8852 - val_AUPRC: 0.5334 - val_f1_score: 0.2894 - val_balanced_accuracy: 0.5857 - val_specificity: 0.9956 - val_miss_rate: 0.8241 - val_fall_out: 0.0044 - val_mcc: 0.3543
Epoch 59/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5752 - accuracy: 0.4236 - recall: 0.1738 - precision: 0.7282 - AUROC: 0.8532 - AUPRC: 0.4563 - f1_score: 0.2807 - balanced_accuracy: 0.5833 - specificity: 0.9928 - miss_rate: 0.8262 - fall_out: 0.0072 - mcc: 0.3275 - val_loss: 1.4458 - val_accuracy: 0.5005 - val_recall: 0.1779 - val_precision: 0.8124 - val_AUROC: 0.8854 - val_AUPRC: 0.5342 - val_f1_score: 0.2918 - val_balanced_accuracy: 0.5866 - val_specificity: 0.9954 - val_miss_rate: 0.8221 - val_fall_out: 0.0046 - val_mcc: 0.3553
Epoch 60/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5915 - accuracy: 0.4236 - recall: 0.1670 - precision: 0.7237 - AUROC: 0.8496 - AUPRC: 0.4515 - f1_score: 0.2713 - balanced_accuracy: 0.5799 - specificity: 0.9929 - miss_rate: 0.8330 - fall_out: 0.0071 - mcc: 0.3195 - val_loss: 1.4453 - val_accuracy: 0.5025 - val_recall: 0.1713 - val_precision: 0.8085 - val_AUROC: 0.8855 - val_AUPRC: 0.5346 - val_f1_score: 0.2828 - val_balanced_accuracy: 0.5834 - val_specificity: 0.9955 - val_miss_rate: 0.8287 - val_fall_out: 0.0045 - val_mcc: 0.3475
Epoch 61/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5897 - accuracy: 0.4170 - recall: 0.1702 - precision: 0.7130 - AUROC: 0.8508 - AUPRC: 0.4488 - f1_score: 0.2748 - balanced_accuracy: 0.5813 - specificity: 0.9924 - miss_rate: 0.8298 - fall_out: 0.0076 - mcc: 0.3196 - val_loss: 1.4434 - val_accuracy: 0.5020 - val_recall: 0.1779 - val_precision: 0.8218 - val_AUROC: 0.8858 - val_AUPRC: 0.5371 - val_f1_score: 0.2924 - val_balanced_accuracy: 0.5868 - val_specificity: 0.9957 - val_miss_rate: 0.8221 - val_fall_out: 0.0043 - val_mcc: 0.3578
Epoch 62/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5847 - accuracy: 0.4178 - recall: 0.1700 - precision: 0.7210 - AUROC: 0.8516 - AUPRC: 0.4513 - f1_score: 0.2751 - balanced_accuracy: 0.5813 - specificity: 0.9927 - miss_rate: 0.8300 - fall_out: 0.0073 - mcc: 0.3216 - val_loss: 1.4415 - val_accuracy: 0.5005 - val_recall: 0.1748 - val_precision: 0.8079 - val_AUROC: 0.8863 - val_AUPRC: 0.5371 - val_f1_score: 0.2875 - val_balanced_accuracy: 0.5851 - val_specificity: 0.9954 - val_miss_rate: 0.8252 - val_fall_out: 0.0046 - val_mcc: 0.3510
Epoch 63/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5768 - accuracy: 0.4236 - recall: 0.1727 - precision: 0.7152 - AUROC: 0.8530 - AUPRC: 0.4583 - f1_score: 0.2782 - balanced_accuracy: 0.5825 - specificity: 0.9924 - miss_rate: 0.8273 - fall_out: 0.0076 - mcc: 0.3226 - val_loss: 1.4387 - val_accuracy: 0.4995 - val_recall: 0.1799 - val_precision: 0.8067 - val_AUROC: 0.8864 - val_AUPRC: 0.5376 - val_f1_score: 0.2941 - val_balanced_accuracy: 0.5875 - val_specificity: 0.9952 - val_miss_rate: 0.8201 - val_fall_out: 0.0048 - val_mcc: 0.3557
Epoch 64/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5689 - accuracy: 0.4205 - recall: 0.1748 - precision: 0.7267 - AUROC: 0.8542 - AUPRC: 0.4567 - f1_score: 0.2819 - balanced_accuracy: 0.5838 - specificity: 0.9927 - miss_rate: 0.8252 - fall_out: 0.0073 - mcc: 0.3280 - val_loss: 1.4373 - val_accuracy: 0.5060 - val_recall: 0.1799 - val_precision: 0.8141 - val_AUROC: 0.8866 - val_AUPRC: 0.5391 - val_f1_score: 0.2946 - val_balanced_accuracy: 0.5876 - val_specificity: 0.9954 - val_miss_rate: 0.8201 - val_fall_out: 0.0046 - val_mcc: 0.3578
Epoch 65/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5829 - accuracy: 0.4221 - recall: 0.1748 - precision: 0.7126 - AUROC: 0.8518 - AUPRC: 0.4483 - f1_score: 0.2808 - balanced_accuracy: 0.5835 - specificity: 0.9922 - miss_rate: 0.8252 - fall_out: 0.0078 - mcc: 0.3239 - val_loss: 1.4369 - val_accuracy: 0.5040 - val_recall: 0.1799 - val_precision: 0.8159 - val_AUROC: 0.8867 - val_AUPRC: 0.5391 - val_f1_score: 0.2947 - val_balanced_accuracy: 0.5877 - val_specificity: 0.9955 - val_miss_rate: 0.8201 - val_fall_out: 0.0045 - val_mcc: 0.3583
Epoch 66/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5743 - accuracy: 0.4269 - recall: 0.1747 - precision: 0.7176 - AUROC: 0.8538 - AUPRC: 0.4597 - f1_score: 0.2810 - balanced_accuracy: 0.5835 - specificity: 0.9924 - miss_rate: 0.8253 - fall_out: 0.0076 - mcc: 0.3252 - val_loss: 1.4337 - val_accuracy: 0.5040 - val_recall: 0.1809 - val_precision: 0.8131 - val_AUROC: 0.8874 - val_AUPRC: 0.5405 - val_f1_score: 0.2959 - val_balanced_accuracy: 0.5881 - val_specificity: 0.9954 - val_miss_rate: 0.8191 - val_fall_out: 0.0046 - val_mcc: 0.3585
Epoch 67/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5790 - accuracy: 0.4226 - recall: 0.1750 - precision: 0.7257 - AUROC: 0.8521 - AUPRC: 0.4547 - f1_score: 0.2820 - balanced_accuracy: 0.5838 - specificity: 0.9927 - miss_rate: 0.8250 - fall_out: 0.0073 - mcc: 0.3278 - val_loss: 1.4343 - val_accuracy: 0.5070 - val_recall: 0.1794 - val_precision: 0.8136 - val_AUROC: 0.8873 - val_AUPRC: 0.5409 - val_f1_score: 0.2939 - val_balanced_accuracy: 0.5874 - val_specificity: 0.9954 - val_miss_rate: 0.8206 - val_fall_out: 0.0046 - val_mcc: 0.3571
Epoch 68/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5672 - accuracy: 0.4302 - recall: 0.1781 - precision: 0.7296 - AUROC: 0.8543 - AUPRC: 0.4612 - f1_score: 0.2863 - balanced_accuracy: 0.5854 - specificity: 0.9927 - miss_rate: 0.8219 - fall_out: 0.0073 - mcc: 0.3320 - val_loss: 1.4325 - val_accuracy: 0.5115 - val_recall: 0.1784 - val_precision: 0.8146 - val_AUROC: 0.8878 - val_AUPRC: 0.5416 - val_f1_score: 0.2926 - val_balanced_accuracy: 0.5869 - val_specificity: 0.9955 - val_miss_rate: 0.8216 - val_fall_out: 0.0045 - val_mcc: 0.3564
Epoch 69/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5672 - accuracy: 0.4195 - recall: 0.1764 - precision: 0.7235 - AUROC: 0.8547 - AUPRC: 0.4586 - f1_score: 0.2836 - balanced_accuracy: 0.5844 - specificity: 0.9925 - miss_rate: 0.8236 - fall_out: 0.0075 - mcc: 0.3285 - val_loss: 1.4286 - val_accuracy: 0.5105 - val_recall: 0.1844 - val_precision: 0.8088 - val_AUROC: 0.8882 - val_AUPRC: 0.5430 - val_f1_score: 0.3003 - val_balanced_accuracy: 0.5898 - val_specificity: 0.9952 - val_miss_rate: 0.8156 - val_fall_out: 0.0048 - val_mcc: 0.3609
Epoch 70/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5728 - accuracy: 0.4223 - recall: 0.1781 - precision: 0.7153 - AUROC: 0.8537 - AUPRC: 0.4562 - f1_score: 0.2852 - balanced_accuracy: 0.5851 - specificity: 0.9921 - miss_rate: 0.8219 - fall_out: 0.0079 - mcc: 0.3277 - val_loss: 1.4286 - val_accuracy: 0.5115 - val_recall: 0.1809 - val_precision: 0.8112 - val_AUROC: 0.8881 - val_AUPRC: 0.5427 - val_f1_score: 0.2958 - val_balanced_accuracy: 0.5881 - val_specificity: 0.9953 - val_miss_rate: 0.8191 - val_fall_out: 0.0047 - val_mcc: 0.3580
Epoch 71/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5646 - accuracy: 0.4292 - recall: 0.1796 - precision: 0.7231 - AUROC: 0.8554 - AUPRC: 0.4608 - f1_score: 0.2877 - balanced_accuracy: 0.5860 - specificity: 0.9924 - miss_rate: 0.8204 - fall_out: 0.0076 - mcc: 0.3315 - val_loss: 1.4260 - val_accuracy: 0.5125 - val_recall: 0.1864 - val_precision: 0.8140 - val_AUROC: 0.8883 - val_AUPRC: 0.5439 - val_f1_score: 0.3033 - val_balanced_accuracy: 0.5908 - val_specificity: 0.9953 - val_miss_rate: 0.8136 - val_fall_out: 0.0047 - val_mcc: 0.3643
Epoch 72/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5717 - accuracy: 0.4236 - recall: 0.1824 - precision: 0.7287 - AUROC: 0.8531 - AUPRC: 0.4560 - f1_score: 0.2917 - balanced_accuracy: 0.5874 - specificity: 0.9925 - miss_rate: 0.8176 - fall_out: 0.0075 - mcc: 0.3358 - val_loss: 1.4285 - val_accuracy: 0.5130 - val_recall: 0.1814 - val_precision: 0.8172 - val_AUROC: 0.8882 - val_AUPRC: 0.5447 - val_f1_score: 0.2968 - val_balanced_accuracy: 0.5884 - val_specificity: 0.9955 - val_miss_rate: 0.8186 - val_fall_out: 0.0045 - val_mcc: 0.3602
Epoch 73/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5712 - accuracy: 0.4251 - recall: 0.1801 - precision: 0.7299 - AUROC: 0.8534 - AUPRC: 0.4597 - f1_score: 0.2889 - balanced_accuracy: 0.5864 - specificity: 0.9926 - miss_rate: 0.8199 - fall_out: 0.0074 - mcc: 0.3340 - val_loss: 1.4250 - val_accuracy: 0.5125 - val_recall: 0.1819 - val_precision: 0.8176 - val_AUROC: 0.8890 - val_AUPRC: 0.5466 - val_f1_score: 0.2975 - val_balanced_accuracy: 0.5887 - val_specificity: 0.9955 - val_miss_rate: 0.8181 - val_fall_out: 0.0045 - val_mcc: 0.3608
Epoch 74/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5668 - accuracy: 0.4178 - recall: 0.1820 - precision: 0.7283 - AUROC: 0.8543 - AUPRC: 0.4604 - f1_score: 0.2912 - balanced_accuracy: 0.5872 - specificity: 0.9925 - miss_rate: 0.8180 - fall_out: 0.0075 - mcc: 0.3353 - val_loss: 1.4251 - val_accuracy: 0.5155 - val_recall: 0.1829 - val_precision: 0.8184 - val_AUROC: 0.8890 - val_AUPRC: 0.5463 - val_f1_score: 0.2989 - val_balanced_accuracy: 0.5892 - val_specificity: 0.9955 - val_miss_rate: 0.8171 - val_fall_out: 0.0045 - val_mcc: 0.3620
Epoch 75/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5623 - accuracy: 0.4216 - recall: 0.1805 - precision: 0.7378 - AUROC: 0.8560 - AUPRC: 0.4639 - f1_score: 0.2900 - balanced_accuracy: 0.5867 - specificity: 0.9929 - miss_rate: 0.8195 - fall_out: 0.0071 - mcc: 0.3367 - val_loss: 1.4232 - val_accuracy: 0.5175 - val_recall: 0.1869 - val_precision: 0.8198 - val_AUROC: 0.8891 - val_AUPRC: 0.5472 - val_f1_score: 0.3044 - val_balanced_accuracy: 0.5912 - val_specificity: 0.9954 - val_miss_rate: 0.8131 - val_fall_out: 0.0046 - val_mcc: 0.3664
Epoch 76/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5652 - accuracy: 0.4294 - recall: 0.1834 - precision: 0.7222 - AUROC: 0.8551 - AUPRC: 0.4631 - f1_score: 0.2925 - balanced_accuracy: 0.5878 - specificity: 0.9922 - miss_rate: 0.8166 - fall_out: 0.0078 - mcc: 0.3348 - val_loss: 1.4219 - val_accuracy: 0.5170 - val_recall: 0.1859 - val_precision: 0.8190 - val_AUROC: 0.8894 - val_AUPRC: 0.5476 - val_f1_score: 0.3030 - val_balanced_accuracy: 0.5907 - val_specificity: 0.9954 - val_miss_rate: 0.8141 - val_fall_out: 0.0046 - val_mcc: 0.3652
Epoch 77/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5741 - accuracy: 0.4241 - recall: 0.1775 - precision: 0.7270 - AUROC: 0.8524 - AUPRC: 0.4594 - f1_score: 0.2853 - balanced_accuracy: 0.5850 - specificity: 0.9926 - miss_rate: 0.8225 - fall_out: 0.0074 - mcc: 0.3306 - val_loss: 1.4219 - val_accuracy: 0.5120 - val_recall: 0.1874 - val_precision: 0.8113 - val_AUROC: 0.8891 - val_AUPRC: 0.5467 - val_f1_score: 0.3044 - val_balanced_accuracy: 0.5913 - val_specificity: 0.9952 - val_miss_rate: 0.8126 - val_fall_out: 0.0048 - val_mcc: 0.3646
Epoch 78/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5747 - accuracy: 0.4226 - recall: 0.1789 - precision: 0.7365 - AUROC: 0.8535 - AUPRC: 0.4568 - f1_score: 0.2878 - balanced_accuracy: 0.5859 - specificity: 0.9929 - miss_rate: 0.8211 - fall_out: 0.0071 - mcc: 0.3347 - val_loss: 1.4212 - val_accuracy: 0.5165 - val_recall: 0.1869 - val_precision: 0.8216 - val_AUROC: 0.8893 - val_AUPRC: 0.5475 - val_f1_score: 0.3045 - val_balanced_accuracy: 0.5912 - val_specificity: 0.9955 - val_miss_rate: 0.8131 - val_fall_out: 0.0045 - val_mcc: 0.3670
Epoch 79/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5648 - accuracy: 0.4245 - recall: 0.1787 - precision: 0.7189 - AUROC: 0.8552 - AUPRC: 0.4619 - f1_score: 0.2863 - balanced_accuracy: 0.5855 - specificity: 0.9922 - miss_rate: 0.8213 - fall_out: 0.0078 - mcc: 0.3294 - val_loss: 1.4191 - val_accuracy: 0.5150 - val_recall: 0.1874 - val_precision: 0.8130 - val_AUROC: 0.8896 - val_AUPRC: 0.5488 - val_f1_score: 0.3046 - val_balanced_accuracy: 0.5913 - val_specificity: 0.9952 - val_miss_rate: 0.8126 - val_fall_out: 0.0048 - val_mcc: 0.3651
Epoch 80/100
63/63 [==============================] - 1s 9ms/step - loss: 1.5585 - accuracy: 0.4262 - recall: 0.1851 - precision: 0.7350 - AUROC: 0.8568 - AUPRC: 0.4629 - f1_score: 0.2957 - balanced_accuracy: 0.5889 - specificity: 0.9926 - miss_rate: 0.8149 - fall_out: 0.0074 - mcc: 0.3402 - val_loss: 1.4172 - val_accuracy: 0.5165 - val_recall: 0.1879 - val_precision: 0.8134 - val_AUROC: 0.8899 - val_AUPRC: 0.5493 - val_f1_score: 0.3053 - val_balanced_accuracy: 0.5915 - val_specificity: 0.9952 - val_miss_rate: 0.8121 - val_fall_out: 0.0048 - val_mcc: 0.3657
Epoch 81/100
63/63 [==============================] - 0s 8ms/step - loss: 1.5657 - accuracy: 0.4241 - recall: 0.1834 - precision: 0.7255 - AUROC: 0.8544 - AUPRC: 0.4604 - f1_score: 0.2927 - balanced_accuracy: 0.5878 - specificity: 0.9923 - miss_rate: 0.8166 - fall_out: 0.0077 - mcc: 0.3357 - val_loss: 1.4174 - val_accuracy: 0.5200 - val_recall: 0.1874 - val_precision: 0.8220 - val_AUROC: 0.8900 - val_AUPRC: 0.5500 - val_f1_score: 0.3052 - val_balanced_accuracy: 0.5914 - val_specificity: 0.9955 - val_miss_rate: 0.8126 - val_fall_out: 0.0045 - val_mcc: 0.3676
Epoch 82/100
63/63 [==============================] - 0s 8ms/step - loss: 1.5699 - accuracy: 0.4190 - recall: 0.1849 - precision: 0.7289 - AUROC: 0.8538 - AUPRC: 0.4590 - f1_score: 0.2949 - balanced_accuracy: 0.5886 - specificity: 0.9924 - miss_rate: 0.8151 - fall_out: 0.0076 - mcc: 0.3382 - val_loss: 1.4158 - val_accuracy: 0.5155 - val_recall: 0.1894 - val_precision: 0.8129 - val_AUROC: 0.8902 - val_AUPRC: 0.5506 - val_f1_score: 0.3072 - val_balanced_accuracy: 0.5923 - val_specificity: 0.9952 - val_miss_rate: 0.8106 - val_fall_out: 0.0048 - val_mcc: 0.3670
Epoch 83/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5588 - accuracy: 0.4274 - recall: 0.1879 - precision: 0.7278 - AUROC: 0.8562 - AUPRC: 0.4663 - f1_score: 0.2987 - balanced_accuracy: 0.5900 - specificity: 0.9922 - miss_rate: 0.8121 - fall_out: 0.0078 - mcc: 0.3407 - val_loss: 1.4159 - val_accuracy: 0.5160 - val_recall: 0.1904 - val_precision: 0.8137 - val_AUROC: 0.8904 - val_AUPRC: 0.5499 - val_f1_score: 0.3086 - val_balanced_accuracy: 0.5928 - val_specificity: 0.9952 - val_miss_rate: 0.8096 - val_fall_out: 0.0048 - val_mcc: 0.3682
Epoch 84/100
63/63 [==============================] - 1s 8ms/step - loss: 1.5578 - accuracy: 0.4236 - recall: 0.1874 - precision: 0.7269 - AUROC: 0.8572 - AUPRC: 0.4634 - f1_score: 0.2979 - balanced_accuracy: 0.5898 - specificity: 0.9922 - miss_rate: 0.8126 - fall_out: 0.0078 - mcc: 0.3399 - val_loss: 1.4139 - val_accuracy: 0.5120 - val_recall: 0.1889 - val_precision: 0.8178 - val_AUROC: 0.8907 - val_AUPRC: 0.5504 - val_f1_score: 0.3069 - val_balanced_accuracy: 0.5921 - val_specificity: 0.9953 - val_miss_rate: 0.8111 - val_fall_out: 0.0047 - val_mcc: 0.3679
Epoch 85/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5652 - accuracy: 0.4225 - recall: 0.1861 - precision: 0.7210 - AUROC: 0.8547 - AUPRC: 0.4583 - f1_score: 0.2959 - balanced_accuracy: 0.5891 - specificity: 0.9920 - miss_rate: 0.8139 - fall_out: 0.0080 - mcc: 0.3370 - val_loss: 1.4153 - val_accuracy: 0.5170 - val_recall: 0.1889 - val_precision: 0.8231 - val_AUROC: 0.8908 - val_AUPRC: 0.5507 - val_f1_score: 0.3073 - val_balanced_accuracy: 0.5922 - val_specificity: 0.9955 - val_miss_rate: 0.8111 - val_fall_out: 0.0045 - val_mcc: 0.3694
Epoch 86/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5689 - accuracy: 0.4275 - recall: 0.1836 - precision: 0.7265 - AUROC: 0.8543 - AUPRC: 0.4598 - f1_score: 0.2931 - balanced_accuracy: 0.5880 - specificity: 0.9923 - miss_rate: 0.8164 - fall_out: 0.0077 - mcc: 0.3363 - val_loss: 1.4135 - val_accuracy: 0.5140 - val_recall: 0.1894 - val_precision: 0.8182 - val_AUROC: 0.8912 - val_AUPRC: 0.5517 - val_f1_score: 0.3076 - val_balanced_accuracy: 0.5924 - val_specificity: 0.9953 - val_miss_rate: 0.8106 - val_fall_out: 0.0047 - val_mcc: 0.3685
Epoch 87/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5611 - accuracy: 0.4225 - recall: 0.1807 - precision: 0.7306 - AUROC: 0.8555 - AUPRC: 0.4606 - f1_score: 0.2898 - balanced_accuracy: 0.5867 - specificity: 0.9926 - miss_rate: 0.8193 - fall_out: 0.0074 - mcc: 0.3348 - val_loss: 1.4131 - val_accuracy: 0.5135 - val_recall: 0.1884 - val_precision: 0.8192 - val_AUROC: 0.8912 - val_AUPRC: 0.5522 - val_f1_score: 0.3063 - val_balanced_accuracy: 0.5919 - val_specificity: 0.9954 - val_miss_rate: 0.8116 - val_fall_out: 0.0046 - val_mcc: 0.3678
Epoch 88/100
63/63 [==============================] - 1s 9ms/step - loss: 1.5577 - accuracy: 0.4304 - recall: 0.1862 - precision: 0.7365 - AUROC: 0.8569 - AUPRC: 0.4663 - f1_score: 0.2973 - balanced_accuracy: 0.5894 - specificity: 0.9926 - miss_rate: 0.8138 - fall_out: 0.0074 - mcc: 0.3417 - val_loss: 1.4114 - val_accuracy: 0.5130 - val_recall: 0.1904 - val_precision: 0.8172 - val_AUROC: 0.8911 - val_AUPRC: 0.5520 - val_f1_score: 0.3088 - val_balanced_accuracy: 0.5928 - val_specificity: 0.9953 - val_miss_rate: 0.8096 - val_fall_out: 0.0047 - val_mcc: 0.3692
Epoch 89/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5535 - accuracy: 0.4315 - recall: 0.1878 - precision: 0.7352 - AUROC: 0.8576 - AUPRC: 0.4665 - f1_score: 0.2991 - balanced_accuracy: 0.5901 - specificity: 0.9925 - miss_rate: 0.8122 - fall_out: 0.0075 - mcc: 0.3428 - val_loss: 1.4101 - val_accuracy: 0.5125 - val_recall: 0.1894 - val_precision: 0.8182 - val_AUROC: 0.8916 - val_AUPRC: 0.5532 - val_f1_score: 0.3076 - val_balanced_accuracy: 0.5924 - val_specificity: 0.9953 - val_miss_rate: 0.8106 - val_fall_out: 0.0047 - val_mcc: 0.3685
Epoch 90/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5570 - accuracy: 0.4240 - recall: 0.1860 - precision: 0.7326 - AUROC: 0.8560 - AUPRC: 0.4650 - f1_score: 0.2967 - balanced_accuracy: 0.5892 - specificity: 0.9925 - miss_rate: 0.8140 - fall_out: 0.0075 - mcc: 0.3403 - val_loss: 1.4079 - val_accuracy: 0.5115 - val_recall: 0.1924 - val_precision: 0.8136 - val_AUROC: 0.8914 - val_AUPRC: 0.5534 - val_f1_score: 0.3112 - val_balanced_accuracy: 0.5937 - val_specificity: 0.9951 - val_miss_rate: 0.8076 - val_fall_out: 0.0049 - val_mcc: 0.3702
Epoch 91/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5577 - accuracy: 0.4280 - recall: 0.1870 - precision: 0.7340 - AUROC: 0.8562 - AUPRC: 0.4674 - f1_score: 0.2981 - balanced_accuracy: 0.5897 - specificity: 0.9925 - miss_rate: 0.8130 - fall_out: 0.0075 - mcc: 0.3417 - val_loss: 1.4071 - val_accuracy: 0.5110 - val_recall: 0.1924 - val_precision: 0.8118 - val_AUROC: 0.8917 - val_AUPRC: 0.5534 - val_f1_score: 0.3111 - val_balanced_accuracy: 0.5937 - val_specificity: 0.9950 - val_miss_rate: 0.8076 - val_fall_out: 0.0050 - val_mcc: 0.3697
Epoch 92/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5543 - accuracy: 0.4272 - recall: 0.1883 - precision: 0.7314 - AUROC: 0.8579 - AUPRC: 0.4663 - f1_score: 0.2994 - balanced_accuracy: 0.5903 - specificity: 0.9923 - miss_rate: 0.8117 - fall_out: 0.0077 - mcc: 0.3421 - val_loss: 1.4072 - val_accuracy: 0.5150 - val_recall: 0.1929 - val_precision: 0.8157 - val_AUROC: 0.8916 - val_AUPRC: 0.5539 - val_f1_score: 0.3120 - val_balanced_accuracy: 0.5940 - val_specificity: 0.9952 - val_miss_rate: 0.8071 - val_fall_out: 0.0048 - val_mcc: 0.3713
Epoch 93/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5592 - accuracy: 0.4259 - recall: 0.1881 - precision: 0.7235 - AUROC: 0.8563 - AUPRC: 0.4632 - f1_score: 0.2986 - balanced_accuracy: 0.5901 - specificity: 0.9920 - miss_rate: 0.8119 - fall_out: 0.0080 - mcc: 0.3396 - val_loss: 1.4069 - val_accuracy: 0.5140 - val_recall: 0.1944 - val_precision: 0.8151 - val_AUROC: 0.8917 - val_AUPRC: 0.5533 - val_f1_score: 0.3139 - val_balanced_accuracy: 0.5947 - val_specificity: 0.9951 - val_miss_rate: 0.8056 - val_fall_out: 0.0049 - val_mcc: 0.3726
Epoch 94/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5555 - accuracy: 0.4257 - recall: 0.1835 - precision: 0.7157 - AUROC: 0.8569 - AUPRC: 0.4629 - f1_score: 0.2921 - balanced_accuracy: 0.5877 - specificity: 0.9919 - miss_rate: 0.8165 - fall_out: 0.0081 - mcc: 0.3329 - val_loss: 1.4058 - val_accuracy: 0.5140 - val_recall: 0.1949 - val_precision: 0.8087 - val_AUROC: 0.8921 - val_AUPRC: 0.5544 - val_f1_score: 0.3141 - val_balanced_accuracy: 0.5949 - val_specificity: 0.9949 - val_miss_rate: 0.8051 - val_fall_out: 0.0051 - val_mcc: 0.3712
Epoch 95/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5487 - accuracy: 0.4276 - recall: 0.1875 - precision: 0.7232 - AUROC: 0.8588 - AUPRC: 0.4668 - f1_score: 0.2978 - balanced_accuracy: 0.5898 - specificity: 0.9920 - miss_rate: 0.8125 - fall_out: 0.0080 - mcc: 0.3389 - val_loss: 1.4071 - val_accuracy: 0.5165 - val_recall: 0.1934 - val_precision: 0.8195 - val_AUROC: 0.8922 - val_AUPRC: 0.5543 - val_f1_score: 0.3129 - val_balanced_accuracy: 0.5943 - val_specificity: 0.9953 - val_miss_rate: 0.8066 - val_fall_out: 0.0047 - val_mcc: 0.3729
Epoch 96/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5540 - accuracy: 0.4262 - recall: 0.1852 - precision: 0.7340 - AUROC: 0.8568 - AUPRC: 0.4646 - f1_score: 0.2958 - balanced_accuracy: 0.5889 - specificity: 0.9925 - miss_rate: 0.8148 - fall_out: 0.0075 - mcc: 0.3400 - val_loss: 1.4026 - val_accuracy: 0.5135 - val_recall: 0.1969 - val_precision: 0.8103 - val_AUROC: 0.8927 - val_AUPRC: 0.5553 - val_f1_score: 0.3168 - val_balanced_accuracy: 0.5959 - val_specificity: 0.9949 - val_miss_rate: 0.8031 - val_fall_out: 0.0051 - val_mcc: 0.3736
Epoch 97/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5533 - accuracy: 0.4299 - recall: 0.1878 - precision: 0.7263 - AUROC: 0.8574 - AUPRC: 0.4655 - f1_score: 0.2984 - balanced_accuracy: 0.5899 - specificity: 0.9921 - miss_rate: 0.8122 - fall_out: 0.0079 - mcc: 0.3401 - val_loss: 1.4018 - val_accuracy: 0.5130 - val_recall: 0.1974 - val_precision: 0.8140 - val_AUROC: 0.8927 - val_AUPRC: 0.5561 - val_f1_score: 0.3177 - val_balanced_accuracy: 0.5962 - val_specificity: 0.9950 - val_miss_rate: 0.8026 - val_fall_out: 0.0050 - val_mcc: 0.3752
Epoch 98/100
63/63 [==============================] - 1s 9ms/step - loss: 1.5628 - accuracy: 0.4231 - recall: 0.1859 - precision: 0.7314 - AUROC: 0.8552 - AUPRC: 0.4658 - f1_score: 0.2964 - balanced_accuracy: 0.5891 - specificity: 0.9924 - miss_rate: 0.8141 - fall_out: 0.0076 - mcc: 0.3399 - val_loss: 1.4012 - val_accuracy: 0.5130 - val_recall: 0.1959 - val_precision: 0.8180 - val_AUROC: 0.8928 - val_AUPRC: 0.5577 - val_f1_score: 0.3161 - val_balanced_accuracy: 0.5955 - val_specificity: 0.9952 - val_miss_rate: 0.8041 - val_fall_out: 0.0048 - val_mcc: 0.3749
Epoch 99/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5607 - accuracy: 0.4274 - recall: 0.1883 - precision: 0.7357 - AUROC: 0.8563 - AUPRC: 0.4675 - f1_score: 0.2998 - balanced_accuracy: 0.5904 - specificity: 0.9925 - miss_rate: 0.8117 - fall_out: 0.0075 - mcc: 0.3434 - val_loss: 1.4006 - val_accuracy: 0.5130 - val_recall: 0.1949 - val_precision: 0.8172 - val_AUROC: 0.8932 - val_AUPRC: 0.5578 - val_f1_score: 0.3147 - val_balanced_accuracy: 0.5950 - val_specificity: 0.9952 - val_miss_rate: 0.8051 - val_fall_out: 0.0048 - val_mcc: 0.3737
Epoch 100/100
63/63 [==============================] - 0s 7ms/step - loss: 1.5443 - accuracy: 0.4307 - recall: 0.1891 - precision: 0.7309 - AUROC: 0.8588 - AUPRC: 0.4746 - f1_score: 0.3005 - balanced_accuracy: 0.5907 - specificity: 0.9923 - miss_rate: 0.8109 - fall_out: 0.0077 - mcc: 0.3427 - val_loss: 1.4000 - val_accuracy: 0.5175 - val_recall: 0.1969 - val_precision: 0.8170 - val_AUROC: 0.8935 - val_AUPRC: 0.5580 - val_f1_score: 0.3173 - val_balanced_accuracy: 0.5960 - val_specificity: 0.9951 - val_miss_rate: 0.8031 - val_fall_out: 0.0049 - val_mcc: 0.3756
250/250 [==============================] - 1s 4ms/step - loss: 1.3617 - accuracy: 0.5266 - recall: 0.2099 - precision: 0.8322 - AUROC: 0.9015 - AUPRC: 0.5794 - f1_score: 0.3353 - balanced_accuracy: 0.6026 - specificity: 0.9953 - miss_rate: 0.7901 - fall_out: 0.0047 - mcc: 0.3926
63/63 [==============================] - 0s 4ms/step - loss: 1.4000 - accuracy: 0.5175 - recall: 0.1969 - precision: 0.8170 - AUROC: 0.8935 - AUPRC: 0.5580 - f1_score: 0.3173 - balanced_accuracy: 0.5960 - specificity: 0.9951 - miss_rate: 0.8031 - fall_out: 0.0049 - mcc: 0.3756
10it [42:58, 257.87s/it]
for window_type in ("window_30s", "window_3s"):
MLP_metrics_estimate_other_features = model_metrics_holdout_estimate(MLP_other_features_metrics[window_type], number_of_splits)
print(f"-- WINDOW {window_type} --")
print(f"MLP Tuned No Mfccs Metrics - {number_of_splits}-holdouts estimate:")
print(f"Accuracy : train - {MLP_metrics_estimate_other_features['accuracy_train']} -- test - {MLP_metrics_estimate_other_features['accuracy_test']}")
print(f"AUROC : train - {MLP_metrics_estimate_other_features['AUROC_train']} -- test - {MLP_metrics_estimate_other_features['AUROC_test']}")
print(f"AUPRC : train - {MLP_metrics_estimate_other_features['AUPRC_train']} -- test - {MLP_metrics_estimate_other_features['AUPRC_test']}")
print("-"*80)
print("MLP - Train history:")
plot_train_history(MLP_other_features_history[window_type])
print("-"*100)
-- WINDOW window_30s -- MLP Tuned No Mfccs Metrics - 10-holdouts estimate: Accuracy : train - 0.5521902292966843 -- test - 0.49649999737739564 AUROC : train - 0.9043975651264191 -- test - 0.8792526304721833 AUPRC : train - 0.5961230337619782 -- test - 0.5214183390140533 -------------------------------------------------------------------------------- MLP - Train history:
---------------------------------------------------------------------------------------------------- -- WINDOW window_3s -- MLP Tuned No Mfccs Metrics - 10-holdouts estimate: Accuracy : train - 0.7172720432281494 -- test - 0.6791583240032196 AUROC : train - 0.9584499299526215 -- test - 0.9480005145072937 AUPRC : train - 0.797723388671875 -- test - 0.7567114174365998 -------------------------------------------------------------------------------- MLP - Train history:
----------------------------------------------------------------------------------------------------
A convolutional neural network consists a set of convolutional layers (convolution+maxPooling) and a set of dense layer. To avoid overfitting of the model dropout layers were added.
To have a statistically sound estimate of an architecture performance, multiple models are built and trained, each with the same architecture, over different portions of the data (holdouts) and, the average performance of those, is considered as an estimate of the overall performance of the architecture.
Hyperparameter tuning is not performed in this case as the literature found specific complex configurations hard to replicate with tuning with the low amount of data provided by the GTZAN dataset. These fixed architectures still provide good results.
Here are the functions to evaluate the model performances and to build and train the model.
These architectures handle well 2D data retaining spatial information, hence the data used to train and evaluate them are:
def build_fixed_CNN_melS_30s(
input_shape: tuple
) -> tf.keras.Model:
""" Returns the fixed CNN model for 30s window Mel spectrogram.
Parameters
------------------------
input_shape: tuple
The shape of the input.
Returns
------------------------
model: model
The fixed model."""
CNN = Sequential(name="CNN_MelS_30s")
CNN.add(layers.Input(input_shape))
CNN.add(layers.Conv2D(
filters = 128,
kernel_size = 3,
padding='same',
activation='relu'))
CNN.add(layers.MaxPooling2D(pool_size=(3),strides=(2),padding='same')),
CNN.add(layers.Conv2D(
filters = 64,
kernel_size = 3,
padding='same',
activation='relu'))
CNN.add(layers.MaxPooling2D(pool_size=(3),strides=(2),padding='same')),
CNN.add(layers.Conv2D(
filters = 64,
kernel_size = 3,
padding='same',
activation='relu'))
CNN.add(layers.MaxPooling2D(pool_size=(3),strides=(2),padding='same')),
CNN.add(layers.Conv2D(
filters = 128,
kernel_size = 3,
padding='same',
activation='relu'))
CNN.add(layers.MaxPooling2D(pool_size=(3),strides=(2),padding='same')),
CNN.add(layers.Flatten())
for i in range(1):
CNN.add(layers.Dense(units = 128, activation='relu'))
CNN.add(layers.Dropout(0.5))
CNN.add(layers.Dense(10, activation='softmax'))
CNN.compile(
optimizer='adam',
loss='categorical_crossentropy',
metrics=get_standard_binary_metrics()
)
CNN.summary()
return CNN
def build_fixed_CNN_S(
input_shape: tuple
) -> tf.keras.Model:
""" Returns the fixed CNN model for 3s window spectrogram.
Parameters
------------------------
input_shape: tuple
The shape of the input.
Returns
------------------------
model: model
The fixed model."""
CNN = Sequential(name="CNN_S_3s")
CNN.add(layers.Input(input_shape))
CNN.add(layers.Conv2D(
filters = 128,
kernel_size = 3,
padding='same',
activation='relu'))
CNN.add(layers.MaxPooling2D(pool_size=(3),strides=(2),padding='same')),
CNN.add(layers.Conv2D(
filters = 64,
kernel_size = 3,
padding='same',
activation='relu'))
CNN.add(layers.MaxPooling2D(pool_size=(3),strides=(2),padding='same')),
CNN.add(layers.Conv2D(
filters = 64,
kernel_size = 3,
padding='same',
activation='relu'))
CNN.add(layers.MaxPooling2D(pool_size=(3),strides=(2),padding='same')),
CNN.add(layers.Conv2D(
filters = 128,
kernel_size = 4,
padding='same',
activation='relu'))
CNN.add(layers.Flatten())
for i in range(2):
CNN.add(layers.Dense(units = 128, activation='relu'))
CNN.add(layers.Dropout(0.5))
CNN.add(layers.Dense(10, activation='softmax'))
CNN.compile(
optimizer='adam',
loss='categorical_crossentropy',
metrics=get_standard_binary_metrics()
)
CNN.summary()
return CNN
def build_fixed_CNN_melS(
input_shape: tuple
) -> tf.keras.Model:
""" Returns the fixed CNN model for 3s window Mel spectrogram.
Parameters
------------------------
input_shape: tuple
The shape of the input.
Returns
------------------------
model: model
The fixed model."""
CNN = Sequential(name="CNN_MelS_3s")
CNN.add(layers.Input(input_shape))
CNN.add(layers.Conv2D(
filters = 64,
kernel_size = 3,
padding='same',
activation='relu'))
CNN.add(layers.MaxPooling2D(pool_size=(3),strides=(2),padding='same')),
CNN.add(layers.Conv2D(
filters = 64,
kernel_size = 3,
padding='same',
activation='relu'))
CNN.add(layers.MaxPooling2D(pool_size=(3),strides=(2),padding='same')),
CNN.add(layers.Conv2D(
filters = 128,
kernel_size = 3,
padding='same',
activation='relu'))
CNN.add(layers.MaxPooling2D(pool_size=(3),strides=(2),padding='same')),
CNN.add(layers.Conv2D(
filters = 128,
kernel_size = 4,
padding='same',
activation='relu'))
CNN.add(layers.Flatten())
for i in range(2):
CNN.add(layers.Dense(units = 128, activation='relu'))
CNN.add(layers.Dropout(0.5))
CNN.add(layers.Dense(10, activation='softmax'))
CNN.compile(
optimizer='adam',
loss='categorical_crossentropy',
metrics=get_standard_binary_metrics()
)
CNN.summary()
return CNN
def build_fixed_CNN_mfccs(
input_shape: tuple
) -> tf.keras.Model:
""" Returns the fixed CNN model for 3s window Mfccs.
Parameters
------------------------
input_shape: tuple
The shape of the input.
Returns
------------------------
model: model
The fixed model."""
CNN = Sequential(name="CNN_Mfccs_3s")
CNN.add(layers.Input(input_shape))
CNN.add(layers.Conv2D(
filters = 256,
kernel_size = 3,
padding='same',
activation='relu'))
CNN.add(layers.MaxPooling2D(pool_size=(3),strides=(2),padding='same')),
CNN.add(layers.Conv2D(
filters = 256,
kernel_size = 3,
padding='same',
activation='relu'))
CNN.add(layers.MaxPooling2D(pool_size=(3),strides=(2),padding='same')),
CNN.add(layers.Conv2D(
filters = 256,
kernel_size = 3,
padding='same',
activation='relu'))
CNN.add(layers.MaxPooling2D(pool_size=(3),strides=(2),padding='same')),
CNN.add(layers.Conv2D(
filters = 512,
kernel_size = 3,
padding='same',
activation='relu'))
CNN.add(layers.MaxPooling2D(pool_size=(3),strides=(2),padding='same')),
CNN.add(layers.Flatten())
for i in range(2):
CNN.add(layers.Dense(units = 256, activation='relu'))
CNN.add(layers.Dropout(0.5))
CNN.add(layers.Dense(10, activation='softmax'))
CNN.compile(
optimizer='adam',
loss='categorical_crossentropy',
metrics=get_standard_binary_metrics()
)
CNN.summary()
return CNN
epochs = 100
batch_size = 128
def resize(data, shape):
"""Returns the data resized with the given shape"""
resized_data = data.copy()
for i in range(len(resized_data)):
resized_data[i] = cv2.resize(resized_data[i].astype(np.uint8), shape, interpolation = cv2.INTER_CUBIC)
return resized_data
print("---- 30s window Mel Spectrogram - Fixed CNN ----")
resized_input_data = resize(data['mel_spectrogram_30s'], (100,100))
normalized_input_data = [x/255 for x in resized_input_data] # Normalization in 0-1 range
input_data = [np.expand_dims(x, axis=-1) for x in normalized_input_data]
data_labels = data['labels_30s']
CNN_MelS_30s_metrics = []
CNN_MelS_30s_history = []
# Generate holdouts
for holdout_number, (train_indices, test_indices) in enumerate(tqdm(holdouts_generator.split(input_data, data_labels))):
print(f"-- HOLDOUT {holdout_number+1}")
# Train/Test data
x_train, x_test = np.array([input_data[x] for x in train_indices]), np.array([input_data[x] for x in test_indices])
y_train, y_test = data_labels.iloc[train_indices], data_labels.iloc[test_indices]
#One hot encoding
y_train = one_hot_encoding(y_train, 10)
y_test = one_hot_encoding(y_test, 10)
# Build CNN
CNN = build_fixed_CNN_melS_30s(x_train.shape[1:])
print("- Training model:\n")
CNN_holdout_metrics, CNN_holdout_history = train_model(
CNN,
np.array(x_train),
np.array(x_test),
y_train.values,
y_test.values,
epochs,
batch_size
)
CNN_MelS_30s_metrics.append(CNN_holdout_metrics)
CNN_MelS_30s_history.append(CNN_holdout_history)
---- 30s window Mel Spectrogram - Fixed CNN ----
0it [00:00, ?it/s]
-- HOLDOUT 1
Model: "CNN_MelS_30s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_120 (Conv2D) (None, 100, 100, 128) 1280
max_pooling2d_120 (MaxPooli (None, 50, 50, 128) 0
ng2D)
conv2d_121 (Conv2D) (None, 50, 50, 64) 73792
max_pooling2d_121 (MaxPooli (None, 25, 25, 64) 0
ng2D)
conv2d_122 (Conv2D) (None, 25, 25, 64) 36928
max_pooling2d_122 (MaxPooli (None, 13, 13, 64) 0
ng2D)
conv2d_123 (Conv2D) (None, 13, 13, 128) 73856
max_pooling2d_123 (MaxPooli (None, 7, 7, 128) 0
ng2D)
flatten_30 (Flatten) (None, 6272) 0
dense_64 (Dense) (None, 128) 802944
dropout_32 (Dropout) (None, 128) 0
dense_65 (Dense) (None, 10) 1290
=================================================================
Total params: 990,090
Trainable params: 990,090
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
7/7 [==============================] - 2s 130ms/step - loss: 2.3169 - accuracy: 0.0951 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.4868 - AUPRC: 0.0977 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.3030 - val_accuracy: 0.1000 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5095 - val_AUPRC: 0.1082 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 2/100
7/7 [==============================] - 0s 59ms/step - loss: 2.3061 - accuracy: 0.0951 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.4994 - AUPRC: 0.0977 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.3009 - val_accuracy: 0.0900 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.4939 - val_AUPRC: 0.0983 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 3/100
7/7 [==============================] - 0s 59ms/step - loss: 2.2995 - accuracy: 0.1076 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.5172 - AUPRC: 0.1080 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.2942 - val_accuracy: 0.1000 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5453 - val_AUPRC: 0.1270 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 4/100
7/7 [==============================] - 0s 59ms/step - loss: 2.2859 - accuracy: 0.1101 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.5489 - AUPRC: 0.1169 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.2703 - val_accuracy: 0.1750 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6023 - val_AUPRC: 0.1539 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 5/100
7/7 [==============================] - 0s 59ms/step - loss: 2.2548 - accuracy: 0.1765 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.5928 - AUPRC: 0.1359 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.1953 - val_accuracy: 0.2400 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6639 - val_AUPRC: 0.2203 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 6/100
7/7 [==============================] - 0s 59ms/step - loss: 2.1705 - accuracy: 0.1977 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.6654 - AUPRC: 0.1812 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.1035 - val_accuracy: 0.1850 - val_recall: 0.0150 - val_precision: 0.7500 - val_AUROC: 0.6872 - val_AUPRC: 0.2231 - val_f1_score: 0.0294 - val_balanced_accuracy: 0.5072 - val_specificity: 0.9994 - val_miss_rate: 0.9850 - val_fall_out: 5.5556e-04 - val_mcc: 0.0970
Epoch 7/100
7/7 [==============================] - 0s 59ms/step - loss: 2.1687 - accuracy: 0.2203 - recall: 0.0288 - precision: 0.5000 - AUROC: 0.6664 - AUPRC: 0.2019 - f1_score: 0.0544 - balanced_accuracy: 0.5128 - specificity: 0.9968 - miss_rate: 0.9712 - fall_out: 0.0032 - mcc: 0.1015 - val_loss: 2.0912 - val_accuracy: 0.2350 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7186 - val_AUPRC: 0.2396 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 8/100
7/7 [==============================] - 0s 59ms/step - loss: 2.1245 - accuracy: 0.2466 - recall: 0.0025 - precision: 1.0000 - AUROC: 0.6768 - AUPRC: 0.2159 - f1_score: 0.0050 - balanced_accuracy: 0.5013 - specificity: 1.0000 - miss_rate: 0.9975 - fall_out: 0.0000e+00 - mcc: 0.0475 - val_loss: 2.0629 - val_accuracy: 0.2450 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7341 - val_AUPRC: 0.2800 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 9/100
7/7 [==============================] - 0s 59ms/step - loss: 2.0326 - accuracy: 0.2766 - recall: 0.0075 - precision: 0.5000 - AUROC: 0.7283 - AUPRC: 0.2535 - f1_score: 0.0148 - balanced_accuracy: 0.5033 - specificity: 0.9992 - miss_rate: 0.9925 - fall_out: 8.3438e-04 - mcc: 0.0517 - val_loss: 1.9471 - val_accuracy: 0.3300 - val_recall: 0.0050 - val_precision: 1.0000 - val_AUROC: 0.7665 - val_AUPRC: 0.3124 - val_f1_score: 0.0100 - val_balanced_accuracy: 0.5025 - val_specificity: 1.0000 - val_miss_rate: 0.9950 - val_fall_out: 0.0000e+00 - val_mcc: 0.0671
Epoch 10/100
7/7 [==============================] - 0s 59ms/step - loss: 1.9766 - accuracy: 0.2766 - recall: 0.0401 - precision: 0.6038 - AUROC: 0.7405 - AUPRC: 0.2709 - f1_score: 0.0751 - balanced_accuracy: 0.5186 - specificity: 0.9971 - miss_rate: 0.9599 - fall_out: 0.0029 - mcc: 0.1372 - val_loss: 1.8796 - val_accuracy: 0.3550 - val_recall: 0.0500 - val_precision: 0.5556 - val_AUROC: 0.7954 - val_AUPRC: 0.3262 - val_f1_score: 0.0917 - val_balanced_accuracy: 0.5228 - val_specificity: 0.9956 - val_miss_rate: 0.9500 - val_fall_out: 0.0044 - val_mcc: 0.1447
Epoch 11/100
7/7 [==============================] - 0s 59ms/step - loss: 1.9201 - accuracy: 0.3016 - recall: 0.0839 - precision: 0.5492 - AUROC: 0.7660 - AUPRC: 0.3015 - f1_score: 0.1455 - balanced_accuracy: 0.5381 - specificity: 0.9924 - miss_rate: 0.9161 - fall_out: 0.0076 - mcc: 0.1864 - val_loss: 1.8695 - val_accuracy: 0.3500 - val_recall: 0.0550 - val_precision: 0.5500 - val_AUROC: 0.7913 - val_AUPRC: 0.3434 - val_f1_score: 0.1000 - val_balanced_accuracy: 0.5250 - val_specificity: 0.9950 - val_miss_rate: 0.9450 - val_fall_out: 0.0050 - val_mcc: 0.1508
Epoch 12/100
7/7 [==============================] - 0s 58ms/step - loss: 1.8709 - accuracy: 0.3417 - recall: 0.0914 - precision: 0.6952 - AUROC: 0.7770 - AUPRC: 0.3360 - f1_score: 0.1615 - balanced_accuracy: 0.5435 - specificity: 0.9955 - miss_rate: 0.9086 - fall_out: 0.0045 - mcc: 0.2290 - val_loss: 1.8116 - val_accuracy: 0.4000 - val_recall: 0.0850 - val_precision: 0.6538 - val_AUROC: 0.8090 - val_AUPRC: 0.3690 - val_f1_score: 0.1504 - val_balanced_accuracy: 0.5400 - val_specificity: 0.9950 - val_miss_rate: 0.9150 - val_fall_out: 0.0050 - val_mcc: 0.2119
Epoch 13/100
7/7 [==============================] - 0s 59ms/step - loss: 1.7854 - accuracy: 0.3579 - recall: 0.1277 - precision: 0.6581 - AUROC: 0.8030 - AUPRC: 0.3698 - f1_score: 0.2138 - balanced_accuracy: 0.5601 - specificity: 0.9926 - miss_rate: 0.8723 - fall_out: 0.0074 - mcc: 0.2616 - val_loss: 1.7991 - val_accuracy: 0.3900 - val_recall: 0.1450 - val_precision: 0.6304 - val_AUROC: 0.8067 - val_AUPRC: 0.3687 - val_f1_score: 0.2358 - val_balanced_accuracy: 0.5678 - val_specificity: 0.9906 - val_miss_rate: 0.8550 - val_fall_out: 0.0094 - val_mcc: 0.2713
Epoch 14/100
7/7 [==============================] - 0s 59ms/step - loss: 1.8033 - accuracy: 0.3529 - recall: 0.1252 - precision: 0.6711 - AUROC: 0.7950 - AUPRC: 0.3592 - f1_score: 0.2110 - balanced_accuracy: 0.5592 - specificity: 0.9932 - miss_rate: 0.8748 - fall_out: 0.0068 - mcc: 0.2624 - val_loss: 1.7893 - val_accuracy: 0.3750 - val_recall: 0.1500 - val_precision: 0.8333 - val_AUROC: 0.8009 - val_AUPRC: 0.3856 - val_f1_score: 0.2542 - val_balanced_accuracy: 0.5733 - val_specificity: 0.9967 - val_miss_rate: 0.8500 - val_fall_out: 0.0033 - val_mcc: 0.3309
Epoch 15/100
7/7 [==============================] - 0s 59ms/step - loss: 1.7557 - accuracy: 0.3730 - recall: 0.1227 - precision: 0.6901 - AUROC: 0.8108 - AUPRC: 0.3841 - f1_score: 0.2083 - balanced_accuracy: 0.5583 - specificity: 0.9939 - miss_rate: 0.8773 - fall_out: 0.0061 - mcc: 0.2646 - val_loss: 1.7114 - val_accuracy: 0.3900 - val_recall: 0.1450 - val_precision: 0.8056 - val_AUROC: 0.8296 - val_AUPRC: 0.4174 - val_f1_score: 0.2458 - val_balanced_accuracy: 0.5706 - val_specificity: 0.9961 - val_miss_rate: 0.8550 - val_fall_out: 0.0039 - val_mcc: 0.3184
Epoch 16/100
7/7 [==============================] - 0s 58ms/step - loss: 1.6772 - accuracy: 0.3792 - recall: 0.1602 - precision: 0.6995 - AUROC: 0.8301 - AUPRC: 0.4178 - f1_score: 0.2607 - balanced_accuracy: 0.5763 - specificity: 0.9924 - miss_rate: 0.8398 - fall_out: 0.0076 - mcc: 0.3059 - val_loss: 1.7035 - val_accuracy: 0.4050 - val_recall: 0.1700 - val_precision: 0.6939 - val_AUROC: 0.8265 - val_AUPRC: 0.4208 - val_f1_score: 0.2731 - val_balanced_accuracy: 0.5808 - val_specificity: 0.9917 - val_miss_rate: 0.8300 - val_fall_out: 0.0083 - val_mcc: 0.3137
Epoch 17/100
7/7 [==============================] - 0s 59ms/step - loss: 1.7239 - accuracy: 0.3905 - recall: 0.1564 - precision: 0.6614 - AUROC: 0.8193 - AUPRC: 0.4040 - f1_score: 0.2530 - balanced_accuracy: 0.5738 - specificity: 0.9911 - miss_rate: 0.8436 - fall_out: 0.0089 - mcc: 0.2913 - val_loss: 1.7263 - val_accuracy: 0.3600 - val_recall: 0.1350 - val_precision: 0.7297 - val_AUROC: 0.8242 - val_AUPRC: 0.4073 - val_f1_score: 0.2278 - val_balanced_accuracy: 0.5647 - val_specificity: 0.9944 - val_miss_rate: 0.8650 - val_fall_out: 0.0056 - val_mcc: 0.2882
Epoch 18/100
7/7 [==============================] - 0s 59ms/step - loss: 1.7067 - accuracy: 0.3655 - recall: 0.1339 - precision: 0.7039 - AUROC: 0.8212 - AUPRC: 0.4060 - f1_score: 0.2250 - balanced_accuracy: 0.5638 - specificity: 0.9937 - miss_rate: 0.8661 - fall_out: 0.0063 - mcc: 0.2803 - val_loss: 1.6763 - val_accuracy: 0.4150 - val_recall: 0.1400 - val_precision: 0.8485 - val_AUROC: 0.8322 - val_AUPRC: 0.4305 - val_f1_score: 0.2403 - val_balanced_accuracy: 0.5686 - val_specificity: 0.9972 - val_miss_rate: 0.8600 - val_fall_out: 0.0028 - val_mcc: 0.3232
Epoch 19/100
7/7 [==============================] - 0s 59ms/step - loss: 1.6601 - accuracy: 0.4018 - recall: 0.1690 - precision: 0.6923 - AUROC: 0.8337 - AUPRC: 0.4329 - f1_score: 0.2716 - balanced_accuracy: 0.5803 - specificity: 0.9917 - miss_rate: 0.8310 - fall_out: 0.0083 - mcc: 0.3123 - val_loss: 1.7244 - val_accuracy: 0.3800 - val_recall: 0.1300 - val_precision: 0.7879 - val_AUROC: 0.8169 - val_AUPRC: 0.4100 - val_f1_score: 0.2232 - val_balanced_accuracy: 0.5631 - val_specificity: 0.9961 - val_miss_rate: 0.8700 - val_fall_out: 0.0039 - val_mcc: 0.2970
Epoch 20/100
7/7 [==============================] - 0s 60ms/step - loss: 1.6405 - accuracy: 0.3880 - recall: 0.1665 - precision: 0.7348 - AUROC: 0.8404 - AUPRC: 0.4403 - f1_score: 0.2714 - balanced_accuracy: 0.5799 - specificity: 0.9933 - miss_rate: 0.8335 - fall_out: 0.0067 - mcc: 0.3222 - val_loss: 1.6773 - val_accuracy: 0.3950 - val_recall: 0.1500 - val_precision: 0.7143 - val_AUROC: 0.8319 - val_AUPRC: 0.4256 - val_f1_score: 0.2479 - val_balanced_accuracy: 0.5717 - val_specificity: 0.9933 - val_miss_rate: 0.8500 - val_fall_out: 0.0067 - val_mcc: 0.2999
25/25 [==============================] - 0s 8ms/step - loss: 1.4734 - accuracy: 0.4831 - recall: 0.1915 - precision: 0.8500 - AUROC: 0.8820 - AUPRC: 0.5390 - f1_score: 0.3126 - balanced_accuracy: 0.5939 - specificity: 0.9962 - miss_rate: 0.8085 - fall_out: 0.0038 - mcc: 0.3795
7/7 [==============================] - 0s 8ms/step - loss: 1.6773 - accuracy: 0.3950 - recall: 0.1500 - precision: 0.7143 - AUROC: 0.8319 - AUPRC: 0.4256 - f1_score: 0.2479 - balanced_accuracy: 0.5717 - specificity: 0.9933 - miss_rate: 0.8500 - fall_out: 0.0067 - mcc: 0.2999
1it [00:10, 10.36s/it]
-- HOLDOUT 2
Model: "CNN_MelS_30s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_124 (Conv2D) (None, 100, 100, 128) 1280
max_pooling2d_124 (MaxPooli (None, 50, 50, 128) 0
ng2D)
conv2d_125 (Conv2D) (None, 50, 50, 64) 73792
max_pooling2d_125 (MaxPooli (None, 25, 25, 64) 0
ng2D)
conv2d_126 (Conv2D) (None, 25, 25, 64) 36928
max_pooling2d_126 (MaxPooli (None, 13, 13, 64) 0
ng2D)
conv2d_127 (Conv2D) (None, 13, 13, 128) 73856
max_pooling2d_127 (MaxPooli (None, 7, 7, 128) 0
ng2D)
flatten_31 (Flatten) (None, 6272) 0
dense_66 (Dense) (None, 128) 802944
dropout_33 (Dropout) (None, 128) 0
dense_67 (Dense) (None, 10) 1290
=================================================================
Total params: 990,090
Trainable params: 990,090
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
7/7 [==============================] - 2s 131ms/step - loss: 2.3133 - accuracy: 0.0951 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.4697 - AUPRC: 0.0925 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.3025 - val_accuracy: 0.1000 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5007 - val_AUPRC: 0.1010 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 2/100
7/7 [==============================] - 0s 59ms/step - loss: 2.3013 - accuracy: 0.1377 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.5119 - AUPRC: 0.1065 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.3014 - val_accuracy: 0.1000 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5024 - val_AUPRC: 0.1131 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 3/100
7/7 [==============================] - 0s 59ms/step - loss: 2.3035 - accuracy: 0.1001 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.5016 - AUPRC: 0.1025 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.3000 - val_accuracy: 0.1000 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5161 - val_AUPRC: 0.1203 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 4/100
7/7 [==============================] - 0s 59ms/step - loss: 2.2998 - accuracy: 0.0989 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.5180 - AUPRC: 0.1082 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.2936 - val_accuracy: 0.1450 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5862 - val_AUPRC: 0.1258 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 5/100
7/7 [==============================] - 0s 59ms/step - loss: 2.2876 - accuracy: 0.1289 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.5487 - AUPRC: 0.1285 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.2693 - val_accuracy: 0.2100 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6184 - val_AUPRC: 0.1548 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 6/100
7/7 [==============================] - 0s 59ms/step - loss: 2.2646 - accuracy: 0.1702 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.5898 - AUPRC: 0.1330 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.2155 - val_accuracy: 0.2300 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6845 - val_AUPRC: 0.2359 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 7/100
7/7 [==============================] - 0s 59ms/step - loss: 2.2178 - accuracy: 0.1790 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.6197 - AUPRC: 0.1497 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.1318 - val_accuracy: 0.2750 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7179 - val_AUPRC: 0.2544 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 8/100
7/7 [==============================] - 0s 59ms/step - loss: 2.1801 - accuracy: 0.2128 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.6459 - AUPRC: 0.1788 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.0342 - val_accuracy: 0.3200 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7576 - val_AUPRC: 0.3121 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 9/100
7/7 [==============================] - 0s 59ms/step - loss: 2.0701 - accuracy: 0.2603 - recall: 0.0013 - precision: 1.0000 - AUROC: 0.7081 - AUPRC: 0.2367 - f1_score: 0.0025 - balanced_accuracy: 0.5006 - specificity: 1.0000 - miss_rate: 0.9987 - fall_out: 0.0000e+00 - mcc: 0.0336 - val_loss: 1.8785 - val_accuracy: 0.3000 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7798 - val_AUPRC: 0.3552 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 10/100
7/7 [==============================] - 0s 59ms/step - loss: 1.9672 - accuracy: 0.2816 - recall: 0.0438 - precision: 0.6731 - AUROC: 0.7433 - AUPRC: 0.2725 - f1_score: 0.0823 - balanced_accuracy: 0.5207 - specificity: 0.9976 - miss_rate: 0.9562 - fall_out: 0.0024 - mcc: 0.1546 - val_loss: 1.8055 - val_accuracy: 0.3150 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.8189 - val_AUPRC: 0.3772 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 11/100
7/7 [==============================] - 0s 58ms/step - loss: 1.9774 - accuracy: 0.2628 - recall: 0.0313 - precision: 0.5319 - AUROC: 0.7411 - AUPRC: 0.2555 - f1_score: 0.0591 - balanced_accuracy: 0.5141 - specificity: 0.9969 - miss_rate: 0.9687 - fall_out: 0.0031 - mcc: 0.1107 - val_loss: 1.9112 - val_accuracy: 0.3350 - val_recall: 0.0250 - val_precision: 1.0000 - val_AUROC: 0.7846 - val_AUPRC: 0.3300 - val_f1_score: 0.0488 - val_balanced_accuracy: 0.5125 - val_specificity: 1.0000 - val_miss_rate: 0.9750 - val_fall_out: 0.0000e+00 - val_mcc: 0.1502
Epoch 12/100
7/7 [==============================] - 0s 59ms/step - loss: 1.9831 - accuracy: 0.2328 - recall: 0.0250 - precision: 0.6452 - AUROC: 0.7446 - AUPRC: 0.2454 - f1_score: 0.0482 - balanced_accuracy: 0.5118 - specificity: 0.9985 - miss_rate: 0.9750 - fall_out: 0.0015 - mcc: 0.1134 - val_loss: 1.7912 - val_accuracy: 0.3650 - val_recall: 0.0600 - val_precision: 1.0000 - val_AUROC: 0.8260 - val_AUPRC: 0.3937 - val_f1_score: 0.1132 - val_balanced_accuracy: 0.5300 - val_specificity: 1.0000 - val_miss_rate: 0.9400 - val_fall_out: 0.0000e+00 - val_mcc: 0.2331
Epoch 13/100
7/7 [==============================] - 0s 59ms/step - loss: 1.8780 - accuracy: 0.3116 - recall: 0.0626 - precision: 0.5556 - AUROC: 0.7775 - AUPRC: 0.3014 - f1_score: 0.1125 - balanced_accuracy: 0.5285 - specificity: 0.9944 - miss_rate: 0.9374 - fall_out: 0.0056 - mcc: 0.1621 - val_loss: 1.6776 - val_accuracy: 0.4200 - val_recall: 0.0300 - val_precision: 0.8571 - val_AUROC: 0.8387 - val_AUPRC: 0.4248 - val_f1_score: 0.0580 - val_balanced_accuracy: 0.5147 - val_specificity: 0.9994 - val_miss_rate: 0.9700 - val_fall_out: 5.5556e-04 - val_mcc: 0.1496
Epoch 14/100
7/7 [==============================] - 0s 59ms/step - loss: 1.8286 - accuracy: 0.3141 - recall: 0.0701 - precision: 0.6747 - AUROC: 0.7903 - AUPRC: 0.3212 - f1_score: 0.1270 - balanced_accuracy: 0.5332 - specificity: 0.9962 - miss_rate: 0.9299 - fall_out: 0.0038 - mcc: 0.1963 - val_loss: 1.6682 - val_accuracy: 0.3850 - val_recall: 0.1150 - val_precision: 0.8846 - val_AUROC: 0.8277 - val_AUPRC: 0.4444 - val_f1_score: 0.2035 - val_balanced_accuracy: 0.5567 - val_specificity: 0.9983 - val_miss_rate: 0.8850 - val_fall_out: 0.0017 - val_mcc: 0.3002
Epoch 15/100
7/7 [==============================] - 0s 59ms/step - loss: 1.7748 - accuracy: 0.3242 - recall: 0.0951 - precision: 0.6230 - AUROC: 0.8082 - AUPRC: 0.3536 - f1_score: 0.1650 - balanced_accuracy: 0.5444 - specificity: 0.9936 - miss_rate: 0.9049 - fall_out: 0.0064 - mcc: 0.2171 - val_loss: 1.6829 - val_accuracy: 0.3700 - val_recall: 0.0900 - val_precision: 0.9474 - val_AUROC: 0.8271 - val_AUPRC: 0.4348 - val_f1_score: 0.1644 - val_balanced_accuracy: 0.5447 - val_specificity: 0.9994 - val_miss_rate: 0.9100 - val_fall_out: 5.5556e-04 - val_mcc: 0.2766
Epoch 16/100
7/7 [==============================] - 0s 59ms/step - loss: 1.7649 - accuracy: 0.3342 - recall: 0.0788 - precision: 0.6495 - AUROC: 0.8113 - AUPRC: 0.3519 - f1_score: 0.1406 - balanced_accuracy: 0.5371 - specificity: 0.9953 - miss_rate: 0.9212 - fall_out: 0.0047 - mcc: 0.2030 - val_loss: 1.6311 - val_accuracy: 0.4500 - val_recall: 0.1000 - val_precision: 0.6452 - val_AUROC: 0.8453 - val_AUPRC: 0.4240 - val_f1_score: 0.1732 - val_balanced_accuracy: 0.5469 - val_specificity: 0.9939 - val_miss_rate: 0.9000 - val_fall_out: 0.0061 - val_mcc: 0.2280
Epoch 17/100
7/7 [==============================] - 0s 59ms/step - loss: 1.8025 - accuracy: 0.2979 - recall: 0.0701 - precision: 0.6154 - AUROC: 0.8027 - AUPRC: 0.3294 - f1_score: 0.1258 - balanced_accuracy: 0.5326 - specificity: 0.9951 - miss_rate: 0.9299 - fall_out: 0.0049 - mcc: 0.1844 - val_loss: 1.6171 - val_accuracy: 0.4300 - val_recall: 0.0900 - val_precision: 0.9474 - val_AUROC: 0.8592 - val_AUPRC: 0.4708 - val_f1_score: 0.1644 - val_balanced_accuracy: 0.5447 - val_specificity: 0.9994 - val_miss_rate: 0.9100 - val_fall_out: 5.5556e-04 - val_mcc: 0.2766
Epoch 18/100
7/7 [==============================] - 0s 58ms/step - loss: 1.7046 - accuracy: 0.3705 - recall: 0.0951 - precision: 0.7525 - AUROC: 0.8301 - AUPRC: 0.3982 - f1_score: 0.1689 - balanced_accuracy: 0.5458 - specificity: 0.9965 - miss_rate: 0.9049 - fall_out: 0.0035 - mcc: 0.2461 - val_loss: 1.5517 - val_accuracy: 0.4100 - val_recall: 0.1300 - val_precision: 0.8966 - val_AUROC: 0.8627 - val_AUPRC: 0.4877 - val_f1_score: 0.2271 - val_balanced_accuracy: 0.5642 - val_specificity: 0.9983 - val_miss_rate: 0.8700 - val_fall_out: 0.0017 - val_mcc: 0.3221
Epoch 19/100
7/7 [==============================] - 0s 59ms/step - loss: 1.6548 - accuracy: 0.3717 - recall: 0.1302 - precision: 0.6842 - AUROC: 0.8388 - AUPRC: 0.4110 - f1_score: 0.2187 - balanced_accuracy: 0.5617 - specificity: 0.9933 - miss_rate: 0.8698 - fall_out: 0.0067 - mcc: 0.2712 - val_loss: 1.5283 - val_accuracy: 0.4350 - val_recall: 0.1450 - val_precision: 0.7838 - val_AUROC: 0.8648 - val_AUPRC: 0.4847 - val_f1_score: 0.2447 - val_balanced_accuracy: 0.5703 - val_specificity: 0.9956 - val_miss_rate: 0.8550 - val_fall_out: 0.0044 - val_mcc: 0.3129
Epoch 20/100
7/7 [==============================] - 0s 59ms/step - loss: 1.7106 - accuracy: 0.3830 - recall: 0.1239 - precision: 0.6875 - AUROC: 0.8215 - AUPRC: 0.3785 - f1_score: 0.2100 - balanced_accuracy: 0.5588 - specificity: 0.9937 - miss_rate: 0.8761 - fall_out: 0.0063 - mcc: 0.2653 - val_loss: 1.5215 - val_accuracy: 0.4650 - val_recall: 0.1500 - val_precision: 0.7895 - val_AUROC: 0.8672 - val_AUPRC: 0.5079 - val_f1_score: 0.2521 - val_balanced_accuracy: 0.5728 - val_specificity: 0.9956 - val_miss_rate: 0.8500 - val_fall_out: 0.0044 - val_mcc: 0.3198
Epoch 21/100
7/7 [==============================] - 0s 59ms/step - loss: 1.6984 - accuracy: 0.3805 - recall: 0.1227 - precision: 0.7778 - AUROC: 0.8263 - AUPRC: 0.4164 - f1_score: 0.2119 - balanced_accuracy: 0.5594 - specificity: 0.9961 - miss_rate: 0.8773 - fall_out: 0.0039 - mcc: 0.2860 - val_loss: 1.5481 - val_accuracy: 0.4700 - val_recall: 0.1500 - val_precision: 0.7895 - val_AUROC: 0.8633 - val_AUPRC: 0.4956 - val_f1_score: 0.2521 - val_balanced_accuracy: 0.5728 - val_specificity: 0.9956 - val_miss_rate: 0.8500 - val_fall_out: 0.0044 - val_mcc: 0.3198
Epoch 22/100
7/7 [==============================] - 0s 59ms/step - loss: 1.6360 - accuracy: 0.4005 - recall: 0.1489 - precision: 0.7212 - AUROC: 0.8473 - AUPRC: 0.4361 - f1_score: 0.2469 - balanced_accuracy: 0.5713 - specificity: 0.9936 - miss_rate: 0.8511 - fall_out: 0.0064 - mcc: 0.3007 - val_loss: 1.5102 - val_accuracy: 0.4350 - val_recall: 0.1650 - val_precision: 0.7674 - val_AUROC: 0.8674 - val_AUPRC: 0.4922 - val_f1_score: 0.2716 - val_balanced_accuracy: 0.5797 - val_specificity: 0.9944 - val_miss_rate: 0.8350 - val_fall_out: 0.0056 - val_mcc: 0.3298
Epoch 23/100
7/7 [==============================] - 0s 59ms/step - loss: 1.6359 - accuracy: 0.3855 - recall: 0.1464 - precision: 0.6500 - AUROC: 0.8429 - AUPRC: 0.4181 - f1_score: 0.2390 - balanced_accuracy: 0.5688 - specificity: 0.9912 - miss_rate: 0.8536 - fall_out: 0.0088 - mcc: 0.2783 - val_loss: 1.4674 - val_accuracy: 0.4750 - val_recall: 0.1550 - val_precision: 0.8857 - val_AUROC: 0.8774 - val_AUPRC: 0.5191 - val_f1_score: 0.2638 - val_balanced_accuracy: 0.5764 - val_specificity: 0.9978 - val_miss_rate: 0.8450 - val_fall_out: 0.0022 - val_mcc: 0.3495
Epoch 24/100
7/7 [==============================] - 0s 59ms/step - loss: 1.5666 - accuracy: 0.4456 - recall: 0.1489 - precision: 0.7083 - AUROC: 0.8580 - AUPRC: 0.4555 - f1_score: 0.2461 - balanced_accuracy: 0.5711 - specificity: 0.9932 - miss_rate: 0.8511 - fall_out: 0.0068 - mcc: 0.2972 - val_loss: 1.4827 - val_accuracy: 0.5050 - val_recall: 0.1750 - val_precision: 0.7955 - val_AUROC: 0.8730 - val_AUPRC: 0.5246 - val_f1_score: 0.2869 - val_balanced_accuracy: 0.5850 - val_specificity: 0.9950 - val_miss_rate: 0.8250 - val_fall_out: 0.0050 - val_mcc: 0.3477
Epoch 25/100
7/7 [==============================] - 0s 59ms/step - loss: 1.5410 - accuracy: 0.4406 - recall: 0.1640 - precision: 0.7616 - AUROC: 0.8624 - AUPRC: 0.4667 - f1_score: 0.2698 - balanced_accuracy: 0.5791 - specificity: 0.9943 - miss_rate: 0.8360 - fall_out: 0.0057 - mcc: 0.3271 - val_loss: 1.4478 - val_accuracy: 0.5250 - val_recall: 0.2000 - val_precision: 0.8696 - val_AUROC: 0.8818 - val_AUPRC: 0.5534 - val_f1_score: 0.3252 - val_balanced_accuracy: 0.5983 - val_specificity: 0.9967 - val_miss_rate: 0.8000 - val_fall_out: 0.0033 - val_mcc: 0.3936
Epoch 26/100
7/7 [==============================] - 0s 59ms/step - loss: 1.5407 - accuracy: 0.4180 - recall: 0.1627 - precision: 0.6989 - AUROC: 0.8613 - AUPRC: 0.4625 - f1_score: 0.2640 - balanced_accuracy: 0.5775 - specificity: 0.9922 - miss_rate: 0.8373 - fall_out: 0.0078 - mcc: 0.3082 - val_loss: 1.4058 - val_accuracy: 0.5200 - val_recall: 0.1900 - val_precision: 0.7917 - val_AUROC: 0.8906 - val_AUPRC: 0.5627 - val_f1_score: 0.3065 - val_balanced_accuracy: 0.5922 - val_specificity: 0.9944 - val_miss_rate: 0.8100 - val_fall_out: 0.0056 - val_mcc: 0.3615
Epoch 27/100
7/7 [==============================] - 0s 59ms/step - loss: 1.5275 - accuracy: 0.4481 - recall: 0.1990 - precision: 0.7536 - AUROC: 0.8631 - AUPRC: 0.4923 - f1_score: 0.3149 - balanced_accuracy: 0.5959 - specificity: 0.9928 - miss_rate: 0.8010 - fall_out: 0.0072 - mcc: 0.3588 - val_loss: 1.4307 - val_accuracy: 0.4900 - val_recall: 0.2100 - val_precision: 0.8077 - val_AUROC: 0.8831 - val_AUPRC: 0.5597 - val_f1_score: 0.3333 - val_balanced_accuracy: 0.6022 - val_specificity: 0.9944 - val_miss_rate: 0.7900 - val_fall_out: 0.0056 - val_mcc: 0.3854
Epoch 28/100
7/7 [==============================] - 0s 59ms/step - loss: 1.5186 - accuracy: 0.4431 - recall: 0.2178 - precision: 0.7373 - AUROC: 0.8664 - AUPRC: 0.4921 - f1_score: 0.3362 - balanced_accuracy: 0.6046 - specificity: 0.9914 - miss_rate: 0.7822 - fall_out: 0.0086 - mcc: 0.3706 - val_loss: 1.4871 - val_accuracy: 0.4150 - val_recall: 0.1900 - val_precision: 0.8261 - val_AUROC: 0.8732 - val_AUPRC: 0.5079 - val_f1_score: 0.3089 - val_balanced_accuracy: 0.5928 - val_specificity: 0.9956 - val_miss_rate: 0.8100 - val_fall_out: 0.0044 - val_mcc: 0.3714
25/25 [==============================] - 0s 8ms/step - loss: 1.4146 - accuracy: 0.4856 - recall: 0.1965 - precision: 0.7734 - AUROC: 0.8883 - AUPRC: 0.5396 - f1_score: 0.3134 - balanced_accuracy: 0.5950 - specificity: 0.9936 - miss_rate: 0.8035 - fall_out: 0.0064 - mcc: 0.3624
7/7 [==============================] - 0s 8ms/step - loss: 1.4871 - accuracy: 0.4150 - recall: 0.1900 - precision: 0.8261 - AUROC: 0.8732 - AUPRC: 0.5079 - f1_score: 0.3089 - balanced_accuracy: 0.5928 - specificity: 0.9956 - miss_rate: 0.8100 - fall_out: 0.0044 - mcc: 0.3714
2it [00:23, 12.18s/it]
-- HOLDOUT 3
Model: "CNN_MelS_30s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_128 (Conv2D) (None, 100, 100, 128) 1280
max_pooling2d_128 (MaxPooli (None, 50, 50, 128) 0
ng2D)
conv2d_129 (Conv2D) (None, 50, 50, 64) 73792
max_pooling2d_129 (MaxPooli (None, 25, 25, 64) 0
ng2D)
conv2d_130 (Conv2D) (None, 25, 25, 64) 36928
max_pooling2d_130 (MaxPooli (None, 13, 13, 64) 0
ng2D)
conv2d_131 (Conv2D) (None, 13, 13, 128) 73856
max_pooling2d_131 (MaxPooli (None, 7, 7, 128) 0
ng2D)
flatten_32 (Flatten) (None, 6272) 0
dense_68 (Dense) (None, 128) 802944
dropout_34 (Dropout) (None, 128) 0
dense_69 (Dense) (None, 10) 1290
=================================================================
Total params: 990,090
Trainable params: 990,090
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
7/7 [==============================] - 2s 132ms/step - loss: 2.3182 - accuracy: 0.0788 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.4893 - AUPRC: 0.0951 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.3016 - val_accuracy: 0.1650 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5000 - val_AUPRC: 0.1000 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 2/100
7/7 [==============================] - 0s 59ms/step - loss: 2.2990 - accuracy: 0.1151 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.5251 - AUPRC: 0.1086 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.2996 - val_accuracy: 0.1250 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5338 - val_AUPRC: 0.1092 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 3/100
7/7 [==============================] - 0s 59ms/step - loss: 2.3022 - accuracy: 0.1227 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.5200 - AUPRC: 0.1059 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.2885 - val_accuracy: 0.1200 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5669 - val_AUPRC: 0.1223 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 4/100
7/7 [==============================] - 0s 59ms/step - loss: 2.2690 - accuracy: 0.1527 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.5990 - AUPRC: 0.1370 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.2322 - val_accuracy: 0.1700 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5978 - val_AUPRC: 0.1840 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 5/100
7/7 [==============================] - 0s 59ms/step - loss: 2.2048 - accuracy: 0.1952 - recall: 0.0050 - precision: 0.8000 - AUROC: 0.6249 - AUPRC: 0.1638 - f1_score: 0.0100 - balanced_accuracy: 0.5024 - specificity: 0.9999 - miss_rate: 0.9950 - fall_out: 1.3906e-04 - mcc: 0.0584 - val_loss: 2.1344 - val_accuracy: 0.2550 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6914 - val_AUPRC: 0.2308 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 6/100
7/7 [==============================] - 0s 59ms/step - loss: 2.1551 - accuracy: 0.1802 - recall: 0.0013 - precision: 1.0000 - AUROC: 0.6590 - AUPRC: 0.1848 - f1_score: 0.0025 - balanced_accuracy: 0.5006 - specificity: 1.0000 - miss_rate: 0.9987 - fall_out: 0.0000e+00 - mcc: 0.0336 - val_loss: 2.1075 - val_accuracy: 0.2850 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6929 - val_AUPRC: 0.2346 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 7/100
7/7 [==============================] - 0s 58ms/step - loss: 2.0927 - accuracy: 0.1990 - recall: 0.0050 - precision: 0.5714 - AUROC: 0.6957 - AUPRC: 0.2044 - f1_score: 0.0099 - balanced_accuracy: 0.5023 - specificity: 0.9996 - miss_rate: 0.9950 - fall_out: 4.1719e-04 - mcc: 0.0465 - val_loss: 2.0479 - val_accuracy: 0.2450 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7095 - val_AUPRC: 0.2514 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 8/100
7/7 [==============================] - 0s 59ms/step - loss: 2.0581 - accuracy: 0.2215 - recall: 0.0163 - precision: 0.5652 - AUROC: 0.7041 - AUPRC: 0.2262 - f1_score: 0.0316 - balanced_accuracy: 0.5074 - specificity: 0.9986 - miss_rate: 0.9837 - fall_out: 0.0014 - mcc: 0.0833 - val_loss: 2.0403 - val_accuracy: 0.2400 - val_recall: 0.0250 - val_precision: 0.7143 - val_AUROC: 0.7335 - val_AUPRC: 0.2501 - val_f1_score: 0.0483 - val_balanced_accuracy: 0.5119 - val_specificity: 0.9989 - val_miss_rate: 0.9750 - val_fall_out: 0.0011 - val_mcc: 0.1214
Epoch 9/100
7/7 [==============================] - 0s 59ms/step - loss: 2.0153 - accuracy: 0.2478 - recall: 0.0288 - precision: 0.7419 - AUROC: 0.7236 - AUPRC: 0.2559 - f1_score: 0.0554 - balanced_accuracy: 0.5138 - specificity: 0.9989 - miss_rate: 0.9712 - fall_out: 0.0011 - mcc: 0.1335 - val_loss: 2.1207 - val_accuracy: 0.2600 - val_recall: 0.0150 - val_precision: 0.7500 - val_AUROC: 0.6699 - val_AUPRC: 0.2469 - val_f1_score: 0.0294 - val_balanced_accuracy: 0.5072 - val_specificity: 0.9994 - val_miss_rate: 0.9850 - val_fall_out: 5.5556e-04 - val_mcc: 0.0970
Epoch 10/100
7/7 [==============================] - 0s 59ms/step - loss: 2.0312 - accuracy: 0.2628 - recall: 0.0663 - precision: 0.6543 - AUROC: 0.7104 - AUPRC: 0.2593 - f1_score: 0.1205 - balanced_accuracy: 0.5312 - specificity: 0.9961 - miss_rate: 0.9337 - fall_out: 0.0039 - mcc: 0.1870 - val_loss: 2.0055 - val_accuracy: 0.2600 - val_recall: 0.0250 - val_precision: 0.8333 - val_AUROC: 0.7251 - val_AUPRC: 0.2903 - val_f1_score: 0.0485 - val_balanced_accuracy: 0.5122 - val_specificity: 0.9994 - val_miss_rate: 0.9750 - val_fall_out: 5.5556e-04 - val_mcc: 0.1341
Epoch 11/100
7/7 [==============================] - 0s 59ms/step - loss: 1.9650 - accuracy: 0.2678 - recall: 0.0426 - precision: 0.7391 - AUROC: 0.7482 - AUPRC: 0.2859 - f1_score: 0.0805 - balanced_accuracy: 0.5204 - specificity: 0.9983 - miss_rate: 0.9574 - fall_out: 0.0017 - mcc: 0.1621 - val_loss: 1.9443 - val_accuracy: 0.3150 - val_recall: 0.0650 - val_precision: 0.7222 - val_AUROC: 0.7515 - val_AUPRC: 0.3055 - val_f1_score: 0.1193 - val_balanced_accuracy: 0.5311 - val_specificity: 0.9972 - val_miss_rate: 0.9350 - val_fall_out: 0.0028 - val_mcc: 0.1977
Epoch 12/100
7/7 [==============================] - 0s 59ms/step - loss: 1.9660 - accuracy: 0.2904 - recall: 0.0688 - precision: 0.6180 - AUROC: 0.7409 - AUPRC: 0.2863 - f1_score: 0.1239 - balanced_accuracy: 0.5321 - specificity: 0.9953 - miss_rate: 0.9312 - fall_out: 0.0047 - mcc: 0.1832 - val_loss: 1.9069 - val_accuracy: 0.3150 - val_recall: 0.0600 - val_precision: 0.7059 - val_AUROC: 0.7634 - val_AUPRC: 0.3256 - val_f1_score: 0.1106 - val_balanced_accuracy: 0.5286 - val_specificity: 0.9972 - val_miss_rate: 0.9400 - val_fall_out: 0.0028 - val_mcc: 0.1870
Epoch 13/100
7/7 [==============================] - 0s 60ms/step - loss: 1.9336 - accuracy: 0.2753 - recall: 0.0738 - precision: 0.6705 - AUROC: 0.7537 - AUPRC: 0.2927 - f1_score: 0.1330 - balanced_accuracy: 0.5349 - specificity: 0.9960 - miss_rate: 0.9262 - fall_out: 0.0040 - mcc: 0.2007 - val_loss: 1.8925 - val_accuracy: 0.3500 - val_recall: 0.0600 - val_precision: 0.7059 - val_AUROC: 0.7769 - val_AUPRC: 0.3397 - val_f1_score: 0.1106 - val_balanced_accuracy: 0.5286 - val_specificity: 0.9972 - val_miss_rate: 0.9400 - val_fall_out: 0.0028 - val_mcc: 0.1870
Epoch 14/100
7/7 [==============================] - 0s 62ms/step - loss: 1.8606 - accuracy: 0.3217 - recall: 0.0626 - precision: 0.6757 - AUROC: 0.7858 - AUPRC: 0.3343 - f1_score: 0.1145 - balanced_accuracy: 0.5296 - specificity: 0.9967 - miss_rate: 0.9374 - fall_out: 0.0033 - mcc: 0.1855 - val_loss: 1.8679 - val_accuracy: 0.3600 - val_recall: 0.1000 - val_precision: 0.7407 - val_AUROC: 0.7811 - val_AUPRC: 0.3357 - val_f1_score: 0.1762 - val_balanced_accuracy: 0.5481 - val_specificity: 0.9961 - val_miss_rate: 0.9000 - val_fall_out: 0.0039 - val_mcc: 0.2499
Epoch 15/100
7/7 [==============================] - 0s 61ms/step - loss: 1.8587 - accuracy: 0.3079 - recall: 0.0914 - precision: 0.7019 - AUROC: 0.7788 - AUPRC: 0.3279 - f1_score: 0.1617 - balanced_accuracy: 0.5435 - specificity: 0.9957 - miss_rate: 0.9086 - fall_out: 0.0043 - mcc: 0.2304 - val_loss: 1.8148 - val_accuracy: 0.3800 - val_recall: 0.1000 - val_precision: 0.8333 - val_AUROC: 0.7889 - val_AUPRC: 0.3832 - val_f1_score: 0.1786 - val_balanced_accuracy: 0.5489 - val_specificity: 0.9978 - val_miss_rate: 0.9000 - val_fall_out: 0.0022 - val_mcc: 0.2694
Epoch 16/100
7/7 [==============================] - 0s 60ms/step - loss: 1.8305 - accuracy: 0.3154 - recall: 0.1014 - precision: 0.6480 - AUROC: 0.7883 - AUPRC: 0.3373 - f1_score: 0.1753 - balanced_accuracy: 0.5476 - specificity: 0.9939 - miss_rate: 0.8986 - fall_out: 0.0061 - mcc: 0.2303 - val_loss: 1.7475 - val_accuracy: 0.4400 - val_recall: 0.1150 - val_precision: 0.8214 - val_AUROC: 0.8171 - val_AUPRC: 0.4172 - val_f1_score: 0.2018 - val_balanced_accuracy: 0.5561 - val_specificity: 0.9972 - val_miss_rate: 0.8850 - val_fall_out: 0.0028 - val_mcc: 0.2865
Epoch 17/100
7/7 [==============================] - 0s 59ms/step - loss: 1.7509 - accuracy: 0.3467 - recall: 0.1101 - precision: 0.6929 - AUROC: 0.8150 - AUPRC: 0.3855 - f1_score: 0.1901 - balanced_accuracy: 0.5524 - specificity: 0.9946 - miss_rate: 0.8899 - fall_out: 0.0054 - mcc: 0.2512 - val_loss: 1.7686 - val_accuracy: 0.3550 - val_recall: 0.1150 - val_precision: 0.7931 - val_AUROC: 0.8111 - val_AUPRC: 0.3843 - val_f1_score: 0.2009 - val_balanced_accuracy: 0.5558 - val_specificity: 0.9967 - val_miss_rate: 0.8850 - val_fall_out: 0.0033 - val_mcc: 0.2802
Epoch 18/100
7/7 [==============================] - 0s 59ms/step - loss: 1.7269 - accuracy: 0.3605 - recall: 0.1289 - precision: 0.7410 - AUROC: 0.8200 - AUPRC: 0.3916 - f1_score: 0.2196 - balanced_accuracy: 0.5620 - specificity: 0.9950 - miss_rate: 0.8711 - fall_out: 0.0050 - mcc: 0.2843 - val_loss: 1.7149 - val_accuracy: 0.4350 - val_recall: 0.1450 - val_precision: 0.7073 - val_AUROC: 0.8279 - val_AUPRC: 0.4357 - val_f1_score: 0.2407 - val_balanced_accuracy: 0.5692 - val_specificity: 0.9933 - val_miss_rate: 0.8550 - val_fall_out: 0.0067 - val_mcc: 0.2929
Epoch 19/100
7/7 [==============================] - 0s 59ms/step - loss: 1.6769 - accuracy: 0.4030 - recall: 0.1589 - precision: 0.7017 - AUROC: 0.8310 - AUPRC: 0.4230 - f1_score: 0.2592 - balanced_accuracy: 0.5757 - specificity: 0.9925 - miss_rate: 0.8411 - fall_out: 0.0075 - mcc: 0.3053 - val_loss: 1.6622 - val_accuracy: 0.4200 - val_recall: 0.1400 - val_precision: 0.8485 - val_AUROC: 0.8336 - val_AUPRC: 0.4466 - val_f1_score: 0.2403 - val_balanced_accuracy: 0.5686 - val_specificity: 0.9972 - val_miss_rate: 0.8600 - val_fall_out: 0.0028 - val_mcc: 0.3232
Epoch 20/100
7/7 [==============================] - 0s 59ms/step - loss: 1.6691 - accuracy: 0.4043 - recall: 0.1552 - precision: 0.6889 - AUROC: 0.8302 - AUPRC: 0.4198 - f1_score: 0.2533 - balanced_accuracy: 0.5737 - specificity: 0.9922 - miss_rate: 0.8448 - fall_out: 0.0078 - mcc: 0.2980 - val_loss: 1.6733 - val_accuracy: 0.4500 - val_recall: 0.1850 - val_precision: 0.7255 - val_AUROC: 0.8252 - val_AUPRC: 0.4534 - val_f1_score: 0.2948 - val_balanced_accuracy: 0.5886 - val_specificity: 0.9922 - val_miss_rate: 0.8150 - val_fall_out: 0.0078 - val_mcc: 0.3373
Epoch 21/100
7/7 [==============================] - 0s 59ms/step - loss: 1.6249 - accuracy: 0.4155 - recall: 0.1564 - precision: 0.7353 - AUROC: 0.8429 - AUPRC: 0.4419 - f1_score: 0.2580 - balanced_accuracy: 0.5751 - specificity: 0.9937 - miss_rate: 0.8436 - fall_out: 0.0063 - mcc: 0.3122 - val_loss: 1.6251 - val_accuracy: 0.4700 - val_recall: 0.1850 - val_precision: 0.7551 - val_AUROC: 0.8371 - val_AUPRC: 0.4779 - val_f1_score: 0.2972 - val_balanced_accuracy: 0.5892 - val_specificity: 0.9933 - val_miss_rate: 0.8150 - val_fall_out: 0.0067 - val_mcc: 0.3461
Epoch 22/100
7/7 [==============================] - 0s 59ms/step - loss: 1.6030 - accuracy: 0.4080 - recall: 0.1927 - precision: 0.7299 - AUROC: 0.8496 - AUPRC: 0.4658 - f1_score: 0.3050 - balanced_accuracy: 0.5924 - specificity: 0.9921 - miss_rate: 0.8073 - fall_out: 0.0079 - mcc: 0.3458 - val_loss: 1.5786 - val_accuracy: 0.5000 - val_recall: 0.1900 - val_precision: 0.7600 - val_AUROC: 0.8517 - val_AUPRC: 0.4998 - val_f1_score: 0.3040 - val_balanced_accuracy: 0.5917 - val_specificity: 0.9933 - val_miss_rate: 0.8100 - val_fall_out: 0.0067 - val_mcc: 0.3523
Epoch 23/100
7/7 [==============================] - 0s 59ms/step - loss: 1.5771 - accuracy: 0.4205 - recall: 0.1727 - precision: 0.7582 - AUROC: 0.8547 - AUPRC: 0.4683 - f1_score: 0.2813 - balanced_accuracy: 0.5833 - specificity: 0.9939 - miss_rate: 0.8273 - fall_out: 0.0061 - mcc: 0.3350 - val_loss: 1.5568 - val_accuracy: 0.4750 - val_recall: 0.1650 - val_precision: 0.7857 - val_AUROC: 0.8615 - val_AUPRC: 0.4917 - val_f1_score: 0.2727 - val_balanced_accuracy: 0.5800 - val_specificity: 0.9950 - val_miss_rate: 0.8350 - val_fall_out: 0.0050 - val_mcc: 0.3348
Epoch 24/100
7/7 [==============================] - 0s 59ms/step - loss: 1.6072 - accuracy: 0.4393 - recall: 0.1690 - precision: 0.7105 - AUROC: 0.8483 - AUPRC: 0.4578 - f1_score: 0.2730 - balanced_accuracy: 0.5807 - specificity: 0.9924 - miss_rate: 0.8310 - fall_out: 0.0076 - mcc: 0.3176 - val_loss: 1.7258 - val_accuracy: 0.4000 - val_recall: 0.1750 - val_precision: 0.6604 - val_AUROC: 0.8307 - val_AUPRC: 0.4354 - val_f1_score: 0.2767 - val_balanced_accuracy: 0.5825 - val_specificity: 0.9900 - val_miss_rate: 0.8250 - val_fall_out: 0.0100 - val_mcc: 0.3082
Epoch 25/100
7/7 [==============================] - 0s 59ms/step - loss: 1.5967 - accuracy: 0.4268 - recall: 0.1827 - precision: 0.7122 - AUROC: 0.8482 - AUPRC: 0.4571 - f1_score: 0.2908 - balanced_accuracy: 0.5873 - specificity: 0.9918 - miss_rate: 0.8173 - fall_out: 0.0082 - mcc: 0.3311 - val_loss: 1.6408 - val_accuracy: 0.4350 - val_recall: 0.1700 - val_precision: 0.6939 - val_AUROC: 0.8421 - val_AUPRC: 0.4542 - val_f1_score: 0.2731 - val_balanced_accuracy: 0.5808 - val_specificity: 0.9917 - val_miss_rate: 0.8300 - val_fall_out: 0.0083 - val_mcc: 0.3137
25/25 [==============================] - 0s 8ms/step - loss: 1.4134 - accuracy: 0.5169 - recall: 0.2140 - precision: 0.7600 - AUROC: 0.8907 - AUPRC: 0.5495 - f1_score: 0.3340 - balanced_accuracy: 0.6033 - specificity: 0.9925 - miss_rate: 0.7860 - fall_out: 0.0075 - mcc: 0.3745
7/7 [==============================] - 0s 8ms/step - loss: 1.6408 - accuracy: 0.4350 - recall: 0.1700 - precision: 0.6939 - AUROC: 0.8421 - AUPRC: 0.4542 - f1_score: 0.2731 - balanced_accuracy: 0.5808 - specificity: 0.9917 - miss_rate: 0.8300 - fall_out: 0.0083 - mcc: 0.3137
3it [00:36, 12.21s/it]
-- HOLDOUT 4
Model: "CNN_MelS_30s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_132 (Conv2D) (None, 100, 100, 128) 1280
max_pooling2d_132 (MaxPooli (None, 50, 50, 128) 0
ng2D)
conv2d_133 (Conv2D) (None, 50, 50, 64) 73792
max_pooling2d_133 (MaxPooli (None, 25, 25, 64) 0
ng2D)
conv2d_134 (Conv2D) (None, 25, 25, 64) 36928
max_pooling2d_134 (MaxPooli (None, 13, 13, 64) 0
ng2D)
conv2d_135 (Conv2D) (None, 13, 13, 128) 73856
max_pooling2d_135 (MaxPooli (None, 7, 7, 128) 0
ng2D)
flatten_33 (Flatten) (None, 6272) 0
dense_70 (Dense) (None, 128) 802944
dropout_35 (Dropout) (None, 128) 0
dense_71 (Dense) (None, 10) 1290
=================================================================
Total params: 990,090
Trainable params: 990,090
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
7/7 [==============================] - 2s 134ms/step - loss: 2.3073 - accuracy: 0.0989 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.5101 - AUPRC: 0.1023 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.3010 - val_accuracy: 0.1200 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5211 - val_AUPRC: 0.1030 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 2/100
7/7 [==============================] - 0s 60ms/step - loss: 2.2995 - accuracy: 0.0939 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.5187 - AUPRC: 0.1056 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.2884 - val_accuracy: 0.1000 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5698 - val_AUPRC: 0.1323 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 3/100
7/7 [==============================] - 0s 59ms/step - loss: 2.2713 - accuracy: 0.1151 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.5540 - AUPRC: 0.1245 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.2301 - val_accuracy: 0.2050 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6023 - val_AUPRC: 0.1529 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 4/100
7/7 [==============================] - 0s 59ms/step - loss: 2.1990 - accuracy: 0.1852 - recall: 0.0013 - precision: 0.2500 - AUROC: 0.6299 - AUPRC: 0.1548 - f1_score: 0.0025 - balanced_accuracy: 0.5004 - specificity: 0.9996 - miss_rate: 0.9987 - fall_out: 4.1719e-04 - mcc: 0.0112 - val_loss: 2.1702 - val_accuracy: 0.2050 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6818 - val_AUPRC: 0.2066 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 5/100
7/7 [==============================] - 0s 59ms/step - loss: 2.1189 - accuracy: 0.2015 - recall: 0.0113 - precision: 0.3600 - AUROC: 0.6825 - AUPRC: 0.1947 - f1_score: 0.0218 - balanced_accuracy: 0.5045 - specificity: 0.9978 - miss_rate: 0.9887 - fall_out: 0.0022 - mcc: 0.0486 - val_loss: 2.2285 - val_accuracy: 0.2150 - val_recall: 0.0700 - val_precision: 0.4828 - val_AUROC: 0.6501 - val_AUPRC: 0.1870 - val_f1_score: 0.1223 - val_balanced_accuracy: 0.5308 - val_specificity: 0.9917 - val_miss_rate: 0.9300 - val_fall_out: 0.0083 - val_mcc: 0.1548
Epoch 6/100
7/7 [==============================] - 0s 59ms/step - loss: 2.1026 - accuracy: 0.2328 - recall: 0.0388 - precision: 0.4627 - AUROC: 0.6984 - AUPRC: 0.2131 - f1_score: 0.0716 - balanced_accuracy: 0.5169 - specificity: 0.9950 - miss_rate: 0.9612 - fall_out: 0.0050 - mcc: 0.1112 - val_loss: 2.0244 - val_accuracy: 0.2800 - val_recall: 0.0300 - val_precision: 0.6000 - val_AUROC: 0.7512 - val_AUPRC: 0.2796 - val_f1_score: 0.0571 - val_balanced_accuracy: 0.5139 - val_specificity: 0.9978 - val_miss_rate: 0.9700 - val_fall_out: 0.0022 - val_mcc: 0.1181
Epoch 7/100
7/7 [==============================] - 0s 60ms/step - loss: 2.0866 - accuracy: 0.2265 - recall: 0.0138 - precision: 0.4783 - AUROC: 0.6961 - AUPRC: 0.2176 - f1_score: 0.0268 - balanced_accuracy: 0.5060 - specificity: 0.9983 - miss_rate: 0.9862 - fall_out: 0.0017 - mcc: 0.0677 - val_loss: 2.1204 - val_accuracy: 0.2050 - val_recall: 0.0450 - val_precision: 0.4091 - val_AUROC: 0.6869 - val_AUPRC: 0.2009 - val_f1_score: 0.0811 - val_balanced_accuracy: 0.5189 - val_specificity: 0.9928 - val_miss_rate: 0.9550 - val_fall_out: 0.0072 - val_mcc: 0.1087
Epoch 8/100
7/7 [==============================] - 0s 59ms/step - loss: 2.0280 - accuracy: 0.2628 - recall: 0.0275 - precision: 0.5366 - AUROC: 0.7325 - AUPRC: 0.2451 - f1_score: 0.0524 - balanced_accuracy: 0.5124 - specificity: 0.9974 - miss_rate: 0.9725 - fall_out: 0.0026 - mcc: 0.1045 - val_loss: 1.9620 - val_accuracy: 0.2950 - val_recall: 0.0400 - val_precision: 0.6667 - val_AUROC: 0.7646 - val_AUPRC: 0.2998 - val_f1_score: 0.0755 - val_balanced_accuracy: 0.5189 - val_specificity: 0.9978 - val_miss_rate: 0.9600 - val_fall_out: 0.0022 - val_mcc: 0.1468
Epoch 9/100
7/7 [==============================] - 0s 59ms/step - loss: 1.9209 - accuracy: 0.2929 - recall: 0.0688 - precision: 0.5392 - AUROC: 0.7619 - AUPRC: 0.2820 - f1_score: 0.1221 - balanced_accuracy: 0.5312 - specificity: 0.9935 - miss_rate: 0.9312 - fall_out: 0.0065 - mcc: 0.1665 - val_loss: 1.8986 - val_accuracy: 0.2900 - val_recall: 0.0450 - val_precision: 0.6000 - val_AUROC: 0.7636 - val_AUPRC: 0.3168 - val_f1_score: 0.0837 - val_balanced_accuracy: 0.5208 - val_specificity: 0.9967 - val_miss_rate: 0.9550 - val_fall_out: 0.0033 - val_mcc: 0.1449
Epoch 10/100
7/7 [==============================] - 0s 59ms/step - loss: 1.8828 - accuracy: 0.2916 - recall: 0.0551 - precision: 0.6984 - AUROC: 0.7707 - AUPRC: 0.3086 - f1_score: 0.1021 - balanced_accuracy: 0.5262 - specificity: 0.9974 - miss_rate: 0.9449 - fall_out: 0.0026 - mcc: 0.1778 - val_loss: 1.9391 - val_accuracy: 0.2700 - val_recall: 0.0550 - val_precision: 0.6111 - val_AUROC: 0.7488 - val_AUPRC: 0.2898 - val_f1_score: 0.1009 - val_balanced_accuracy: 0.5256 - val_specificity: 0.9961 - val_miss_rate: 0.9450 - val_fall_out: 0.0039 - val_mcc: 0.1624
Epoch 11/100
7/7 [==============================] - 0s 59ms/step - loss: 1.8562 - accuracy: 0.3467 - recall: 0.1076 - precision: 0.6277 - AUROC: 0.7773 - AUPRC: 0.3324 - f1_score: 0.1838 - balanced_accuracy: 0.5503 - specificity: 0.9929 - miss_rate: 0.8924 - fall_out: 0.0071 - mcc: 0.2323 - val_loss: 1.8331 - val_accuracy: 0.3350 - val_recall: 0.1150 - val_precision: 0.7667 - val_AUROC: 0.7873 - val_AUPRC: 0.3471 - val_f1_score: 0.2000 - val_balanced_accuracy: 0.5556 - val_specificity: 0.9961 - val_miss_rate: 0.8850 - val_fall_out: 0.0039 - val_mcc: 0.2742
Epoch 12/100
7/7 [==============================] - 0s 59ms/step - loss: 1.7893 - accuracy: 0.3605 - recall: 0.1076 - precision: 0.7049 - AUROC: 0.7994 - AUPRC: 0.3505 - f1_score: 0.1868 - balanced_accuracy: 0.5513 - specificity: 0.9950 - miss_rate: 0.8924 - fall_out: 0.0050 - mcc: 0.2511 - val_loss: 1.7886 - val_accuracy: 0.3500 - val_recall: 0.1150 - val_precision: 0.6970 - val_AUROC: 0.8028 - val_AUPRC: 0.3630 - val_f1_score: 0.1974 - val_balanced_accuracy: 0.5547 - val_specificity: 0.9944 - val_miss_rate: 0.8850 - val_fall_out: 0.0056 - val_mcc: 0.2577
Epoch 13/100
7/7 [==============================] - 0s 59ms/step - loss: 1.7403 - accuracy: 0.3529 - recall: 0.1364 - precision: 0.6812 - AUROC: 0.8144 - AUPRC: 0.3828 - f1_score: 0.2273 - balanced_accuracy: 0.5647 - specificity: 0.9929 - miss_rate: 0.8636 - fall_out: 0.0071 - mcc: 0.2770 - val_loss: 1.7637 - val_accuracy: 0.3950 - val_recall: 0.1050 - val_precision: 0.7500 - val_AUROC: 0.8149 - val_AUPRC: 0.3996 - val_f1_score: 0.1842 - val_balanced_accuracy: 0.5506 - val_specificity: 0.9961 - val_miss_rate: 0.8950 - val_fall_out: 0.0039 - val_mcc: 0.2582
Epoch 14/100
7/7 [==============================] - 0s 59ms/step - loss: 1.6874 - accuracy: 0.3955 - recall: 0.1126 - precision: 0.7500 - AUROC: 0.8314 - AUPRC: 0.4188 - f1_score: 0.1959 - balanced_accuracy: 0.5542 - specificity: 0.9958 - miss_rate: 0.8874 - fall_out: 0.0042 - mcc: 0.2675 - val_loss: 1.7975 - val_accuracy: 0.3600 - val_recall: 0.1550 - val_precision: 0.5636 - val_AUROC: 0.7998 - val_AUPRC: 0.3603 - val_f1_score: 0.2431 - val_balanced_accuracy: 0.5708 - val_specificity: 0.9867 - val_miss_rate: 0.8450 - val_fall_out: 0.0133 - val_mcc: 0.2599
Epoch 15/100
7/7 [==============================] - 0s 59ms/step - loss: 1.6748 - accuracy: 0.3842 - recall: 0.1552 - precision: 0.6078 - AUROC: 0.8310 - AUPRC: 0.4112 - f1_score: 0.2473 - balanced_accuracy: 0.5720 - specificity: 0.9889 - miss_rate: 0.8448 - fall_out: 0.0111 - mcc: 0.2740 - val_loss: 1.7279 - val_accuracy: 0.3750 - val_recall: 0.1450 - val_precision: 0.6304 - val_AUROC: 0.8166 - val_AUPRC: 0.3893 - val_f1_score: 0.2358 - val_balanced_accuracy: 0.5678 - val_specificity: 0.9906 - val_miss_rate: 0.8550 - val_fall_out: 0.0094 - val_mcc: 0.2713
Epoch 16/100
7/7 [==============================] - 0s 59ms/step - loss: 1.6565 - accuracy: 0.3992 - recall: 0.1577 - precision: 0.7241 - AUROC: 0.8368 - AUPRC: 0.4226 - f1_score: 0.2590 - balanced_accuracy: 0.5755 - specificity: 0.9933 - miss_rate: 0.8423 - fall_out: 0.0067 - mcc: 0.3104 - val_loss: 1.7021 - val_accuracy: 0.4150 - val_recall: 0.1350 - val_precision: 0.7941 - val_AUROC: 0.8254 - val_AUPRC: 0.4136 - val_f1_score: 0.2308 - val_balanced_accuracy: 0.5656 - val_specificity: 0.9961 - val_miss_rate: 0.8650 - val_fall_out: 0.0039 - val_mcc: 0.3043
Epoch 17/100
7/7 [==============================] - 0s 60ms/step - loss: 1.6040 - accuracy: 0.4068 - recall: 0.1402 - precision: 0.6871 - AUROC: 0.8503 - AUPRC: 0.4420 - f1_score: 0.2328 - balanced_accuracy: 0.5665 - specificity: 0.9929 - miss_rate: 0.8598 - fall_out: 0.0071 - mcc: 0.2824 - val_loss: 1.6526 - val_accuracy: 0.4200 - val_recall: 0.1500 - val_precision: 0.7143 - val_AUROC: 0.8359 - val_AUPRC: 0.4275 - val_f1_score: 0.2479 - val_balanced_accuracy: 0.5717 - val_specificity: 0.9933 - val_miss_rate: 0.8500 - val_fall_out: 0.0067 - val_mcc: 0.2999
Epoch 18/100
7/7 [==============================] - 0s 60ms/step - loss: 1.5477 - accuracy: 0.4243 - recall: 0.1777 - precision: 0.6860 - AUROC: 0.8622 - AUPRC: 0.4566 - f1_score: 0.2823 - balanced_accuracy: 0.5843 - specificity: 0.9910 - miss_rate: 0.8223 - fall_out: 0.0090 - mcc: 0.3186 - val_loss: 1.6245 - val_accuracy: 0.4350 - val_recall: 0.1300 - val_precision: 0.7222 - val_AUROC: 0.8446 - val_AUPRC: 0.4346 - val_f1_score: 0.2203 - val_balanced_accuracy: 0.5622 - val_specificity: 0.9944 - val_miss_rate: 0.8700 - val_fall_out: 0.0056 - val_mcc: 0.2808
Epoch 19/100
7/7 [==============================] - 0s 60ms/step - loss: 1.5488 - accuracy: 0.4543 - recall: 0.1915 - precision: 0.7217 - AUROC: 0.8622 - AUPRC: 0.4749 - f1_score: 0.3027 - balanced_accuracy: 0.5916 - specificity: 0.9918 - miss_rate: 0.8085 - fall_out: 0.0082 - mcc: 0.3421 - val_loss: 1.6185 - val_accuracy: 0.4150 - val_recall: 0.1500 - val_precision: 0.6818 - val_AUROC: 0.8456 - val_AUPRC: 0.4495 - val_f1_score: 0.2459 - val_balanced_accuracy: 0.5711 - val_specificity: 0.9922 - val_miss_rate: 0.8500 - val_fall_out: 0.0078 - val_mcc: 0.2909
Epoch 20/100
7/7 [==============================] - 0s 59ms/step - loss: 1.5527 - accuracy: 0.4393 - recall: 0.1740 - precision: 0.7433 - AUROC: 0.8610 - AUPRC: 0.4723 - f1_score: 0.2819 - balanced_accuracy: 0.5836 - specificity: 0.9933 - miss_rate: 0.8260 - fall_out: 0.0067 - mcc: 0.3320 - val_loss: 1.6415 - val_accuracy: 0.3900 - val_recall: 0.1700 - val_precision: 0.6538 - val_AUROC: 0.8390 - val_AUPRC: 0.4342 - val_f1_score: 0.2698 - val_balanced_accuracy: 0.5800 - val_specificity: 0.9900 - val_miss_rate: 0.8300 - val_fall_out: 0.0100 - val_mcc: 0.3016
Epoch 21/100
7/7 [==============================] - 0s 59ms/step - loss: 1.4898 - accuracy: 0.4631 - recall: 0.2190 - precision: 0.7202 - AUROC: 0.8718 - AUPRC: 0.4919 - f1_score: 0.3359 - balanced_accuracy: 0.6048 - specificity: 0.9905 - miss_rate: 0.7810 - fall_out: 0.0095 - mcc: 0.3661 - val_loss: 1.5532 - val_accuracy: 0.4300 - val_recall: 0.1600 - val_precision: 0.7273 - val_AUROC: 0.8587 - val_AUPRC: 0.4693 - val_f1_score: 0.2623 - val_balanced_accuracy: 0.5767 - val_specificity: 0.9933 - val_miss_rate: 0.8400 - val_fall_out: 0.0067 - val_mcc: 0.3136
Epoch 22/100
7/7 [==============================] - 0s 59ms/step - loss: 1.4728 - accuracy: 0.4819 - recall: 0.2428 - precision: 0.6978 - AUROC: 0.8752 - AUPRC: 0.5056 - f1_score: 0.3603 - balanced_accuracy: 0.6156 - specificity: 0.9883 - miss_rate: 0.7572 - fall_out: 0.0117 - mcc: 0.3784 - val_loss: 1.6268 - val_accuracy: 0.3850 - val_recall: 0.1600 - val_precision: 0.6667 - val_AUROC: 0.8398 - val_AUPRC: 0.4301 - val_f1_score: 0.2581 - val_balanced_accuracy: 0.5756 - val_specificity: 0.9911 - val_miss_rate: 0.8400 - val_fall_out: 0.0089 - val_mcc: 0.2962
Epoch 23/100
7/7 [==============================] - 0s 59ms/step - loss: 1.5208 - accuracy: 0.4468 - recall: 0.2078 - precision: 0.7345 - AUROC: 0.8648 - AUPRC: 0.4831 - f1_score: 0.3239 - balanced_accuracy: 0.5997 - specificity: 0.9917 - miss_rate: 0.7922 - fall_out: 0.0083 - mcc: 0.3609 - val_loss: 1.5451 - val_accuracy: 0.4550 - val_recall: 0.1700 - val_precision: 0.7391 - val_AUROC: 0.8592 - val_AUPRC: 0.4803 - val_f1_score: 0.2764 - val_balanced_accuracy: 0.5817 - val_specificity: 0.9933 - val_miss_rate: 0.8300 - val_fall_out: 0.0067 - val_mcc: 0.3269
Epoch 24/100
7/7 [==============================] - 0s 60ms/step - loss: 1.4802 - accuracy: 0.4831 - recall: 0.2416 - precision: 0.7228 - AUROC: 0.8741 - AUPRC: 0.5095 - f1_score: 0.3621 - balanced_accuracy: 0.6156 - specificity: 0.9897 - miss_rate: 0.7584 - fall_out: 0.0103 - mcc: 0.3860 - val_loss: 1.5415 - val_accuracy: 0.4450 - val_recall: 0.1400 - val_precision: 0.6364 - val_AUROC: 0.8610 - val_AUPRC: 0.4653 - val_f1_score: 0.2295 - val_balanced_accuracy: 0.5656 - val_specificity: 0.9911 - val_miss_rate: 0.8600 - val_fall_out: 0.0089 - val_mcc: 0.2682
Epoch 25/100
7/7 [==============================] - 0s 60ms/step - loss: 1.4740 - accuracy: 0.4606 - recall: 0.2178 - precision: 0.7311 - AUROC: 0.8742 - AUPRC: 0.5020 - f1_score: 0.3356 - balanced_accuracy: 0.6044 - specificity: 0.9911 - miss_rate: 0.7822 - fall_out: 0.0089 - mcc: 0.3686 - val_loss: 1.5082 - val_accuracy: 0.4700 - val_recall: 0.1900 - val_precision: 0.8085 - val_AUROC: 0.8673 - val_AUPRC: 0.5003 - val_f1_score: 0.3077 - val_balanced_accuracy: 0.5925 - val_specificity: 0.9950 - val_miss_rate: 0.8100 - val_fall_out: 0.0050 - val_mcc: 0.3664
Epoch 26/100
7/7 [==============================] - 0s 59ms/step - loss: 1.3898 - accuracy: 0.5282 - recall: 0.2240 - precision: 0.7458 - AUROC: 0.8931 - AUPRC: 0.5514 - f1_score: 0.3446 - balanced_accuracy: 0.6078 - specificity: 0.9915 - miss_rate: 0.7760 - fall_out: 0.0085 - mcc: 0.3788 - val_loss: 1.4801 - val_accuracy: 0.5000 - val_recall: 0.2000 - val_precision: 0.7547 - val_AUROC: 0.8726 - val_AUPRC: 0.5087 - val_f1_score: 0.3162 - val_balanced_accuracy: 0.5964 - val_specificity: 0.9928 - val_miss_rate: 0.8000 - val_fall_out: 0.0072 - val_mcc: 0.3601
Epoch 27/100
7/7 [==============================] - 0s 59ms/step - loss: 1.3625 - accuracy: 0.5169 - recall: 0.2741 - precision: 0.7526 - AUROC: 0.8959 - AUPRC: 0.5624 - f1_score: 0.4018 - balanced_accuracy: 0.6320 - specificity: 0.9900 - miss_rate: 0.7259 - fall_out: 0.0100 - mcc: 0.4229 - val_loss: 1.5320 - val_accuracy: 0.4450 - val_recall: 0.2050 - val_precision: 0.7321 - val_AUROC: 0.8607 - val_AUPRC: 0.4874 - val_f1_score: 0.3203 - val_balanced_accuracy: 0.5983 - val_specificity: 0.9917 - val_miss_rate: 0.7950 - val_fall_out: 0.0083 - val_mcc: 0.3576
Epoch 28/100
7/7 [==============================] - 0s 59ms/step - loss: 1.3441 - accuracy: 0.5369 - recall: 0.2854 - precision: 0.7308 - AUROC: 0.8978 - AUPRC: 0.5722 - f1_score: 0.4104 - balanced_accuracy: 0.6368 - specificity: 0.9883 - miss_rate: 0.7146 - fall_out: 0.0117 - mcc: 0.4238 - val_loss: 1.5455 - val_accuracy: 0.4600 - val_recall: 0.1950 - val_precision: 0.6190 - val_AUROC: 0.8605 - val_AUPRC: 0.4655 - val_f1_score: 0.2966 - val_balanced_accuracy: 0.5908 - val_specificity: 0.9867 - val_miss_rate: 0.8050 - val_fall_out: 0.0133 - val_mcc: 0.3120
25/25 [==============================] - 0s 8ms/step - loss: 1.2392 - accuracy: 0.5494 - recall: 0.2804 - precision: 0.7568 - AUROC: 0.9166 - AUPRC: 0.6073 - f1_score: 0.4091 - balanced_accuracy: 0.6352 - specificity: 0.9900 - miss_rate: 0.7196 - fall_out: 0.0100 - mcc: 0.4294
7/7 [==============================] - 0s 8ms/step - loss: 1.5455 - accuracy: 0.4600 - recall: 0.1950 - precision: 0.6190 - AUROC: 0.8605 - AUPRC: 0.4654 - f1_score: 0.2966 - balanced_accuracy: 0.5908 - specificity: 0.9867 - miss_rate: 0.8050 - fall_out: 0.0133 - mcc: 0.3120
4it [00:49, 12.75s/it]
-- HOLDOUT 5
Model: "CNN_MelS_30s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_136 (Conv2D) (None, 100, 100, 128) 1280
max_pooling2d_136 (MaxPooli (None, 50, 50, 128) 0
ng2D)
conv2d_137 (Conv2D) (None, 50, 50, 64) 73792
max_pooling2d_137 (MaxPooli (None, 25, 25, 64) 0
ng2D)
conv2d_138 (Conv2D) (None, 25, 25, 64) 36928
max_pooling2d_138 (MaxPooli (None, 13, 13, 64) 0
ng2D)
conv2d_139 (Conv2D) (None, 13, 13, 128) 73856
max_pooling2d_139 (MaxPooli (None, 7, 7, 128) 0
ng2D)
flatten_34 (Flatten) (None, 6272) 0
dense_72 (Dense) (None, 128) 802944
dropout_36 (Dropout) (None, 128) 0
dense_73 (Dense) (None, 10) 1290
=================================================================
Total params: 990,090
Trainable params: 990,090
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
7/7 [==============================] - 2s 132ms/step - loss: 2.3133 - accuracy: 0.0839 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.4950 - AUPRC: 0.0974 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.3016 - val_accuracy: 0.1000 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5062 - val_AUPRC: 0.1007 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 2/100
7/7 [==============================] - 0s 59ms/step - loss: 2.3039 - accuracy: 0.0876 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.4996 - AUPRC: 0.0994 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.2994 - val_accuracy: 0.1000 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5192 - val_AUPRC: 0.1039 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 3/100
7/7 [==============================] - 0s 59ms/step - loss: 2.2947 - accuracy: 0.1014 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.5346 - AUPRC: 0.1145 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.2824 - val_accuracy: 0.1000 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5585 - val_AUPRC: 0.1529 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 4/100
7/7 [==============================] - 0s 59ms/step - loss: 2.2727 - accuracy: 0.1101 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.5640 - AUPRC: 0.1235 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.2215 - val_accuracy: 0.2000 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6912 - val_AUPRC: 0.2076 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 5/100
7/7 [==============================] - 0s 59ms/step - loss: 2.1851 - accuracy: 0.1952 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.6488 - AUPRC: 0.1655 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.1871 - val_accuracy: 0.1950 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6450 - val_AUPRC: 0.2064 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 6/100
7/7 [==============================] - 0s 59ms/step - loss: 2.1237 - accuracy: 0.2228 - recall: 0.0038 - precision: 0.6000 - AUROC: 0.6776 - AUPRC: 0.2029 - f1_score: 0.0075 - balanced_accuracy: 0.5017 - specificity: 0.9997 - miss_rate: 0.9962 - fall_out: 2.7813e-04 - mcc: 0.0417 - val_loss: 2.1510 - val_accuracy: 0.2100 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6668 - val_AUPRC: 0.1840 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 7/100
7/7 [==============================] - 0s 58ms/step - loss: 2.0854 - accuracy: 0.2403 - recall: 0.0050 - precision: 0.6667 - AUROC: 0.7014 - AUPRC: 0.2185 - f1_score: 0.0099 - balanced_accuracy: 0.5024 - specificity: 0.9997 - miss_rate: 0.9950 - fall_out: 2.7813e-04 - mcc: 0.0518 - val_loss: 1.9962 - val_accuracy: 0.2600 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7430 - val_AUPRC: 0.2669 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 8/100
7/7 [==============================] - 0s 59ms/step - loss: 2.0109 - accuracy: 0.2716 - recall: 0.0200 - precision: 0.5714 - AUROC: 0.7300 - AUPRC: 0.2541 - f1_score: 0.0387 - balanced_accuracy: 0.5092 - specificity: 0.9983 - miss_rate: 0.9800 - fall_out: 0.0017 - mcc: 0.0932 - val_loss: 1.9570 - val_accuracy: 0.3050 - val_recall: 0.0500 - val_precision: 0.7143 - val_AUROC: 0.7477 - val_AUPRC: 0.2940 - val_f1_score: 0.0935 - val_balanced_accuracy: 0.5239 - val_specificity: 0.9978 - val_miss_rate: 0.9500 - val_fall_out: 0.0022 - val_mcc: 0.1719
Epoch 9/100
7/7 [==============================] - 0s 59ms/step - loss: 1.9616 - accuracy: 0.2741 - recall: 0.0701 - precision: 0.6154 - AUROC: 0.7441 - AUPRC: 0.2783 - f1_score: 0.1258 - balanced_accuracy: 0.5326 - specificity: 0.9951 - miss_rate: 0.9299 - fall_out: 0.0049 - mcc: 0.1844 - val_loss: 2.0478 - val_accuracy: 0.2150 - val_recall: 0.0750 - val_precision: 0.5000 - val_AUROC: 0.7227 - val_AUPRC: 0.2400 - val_f1_score: 0.1304 - val_balanced_accuracy: 0.5333 - val_specificity: 0.9917 - val_miss_rate: 0.9250 - val_fall_out: 0.0083 - val_mcc: 0.1645
Epoch 10/100
7/7 [==============================] - 0s 59ms/step - loss: 1.9657 - accuracy: 0.2841 - recall: 0.0526 - precision: 0.6269 - AUROC: 0.7451 - AUPRC: 0.2739 - f1_score: 0.0970 - balanced_accuracy: 0.5245 - specificity: 0.9965 - miss_rate: 0.9474 - fall_out: 0.0035 - mcc: 0.1615 - val_loss: 1.9137 - val_accuracy: 0.2950 - val_recall: 0.0550 - val_precision: 0.7333 - val_AUROC: 0.7888 - val_AUPRC: 0.3131 - val_f1_score: 0.1023 - val_balanced_accuracy: 0.5264 - val_specificity: 0.9978 - val_miss_rate: 0.9450 - val_fall_out: 0.0022 - val_mcc: 0.1835
Epoch 11/100
7/7 [==============================] - 0s 59ms/step - loss: 1.8977 - accuracy: 0.3041 - recall: 0.0613 - precision: 0.7000 - AUROC: 0.7691 - AUPRC: 0.3047 - f1_score: 0.1128 - balanced_accuracy: 0.5292 - specificity: 0.9971 - miss_rate: 0.9387 - fall_out: 0.0029 - mcc: 0.1880 - val_loss: 1.8159 - val_accuracy: 0.3850 - val_recall: 0.0500 - val_precision: 0.7692 - val_AUROC: 0.8178 - val_AUPRC: 0.3806 - val_f1_score: 0.0939 - val_balanced_accuracy: 0.5242 - val_specificity: 0.9983 - val_miss_rate: 0.9500 - val_fall_out: 0.0017 - val_mcc: 0.1804
Epoch 12/100
7/7 [==============================] - 0s 59ms/step - loss: 1.8415 - accuracy: 0.3317 - recall: 0.0864 - precision: 0.6970 - AUROC: 0.7872 - AUPRC: 0.3368 - f1_score: 0.1537 - balanced_accuracy: 0.5411 - specificity: 0.9958 - miss_rate: 0.9136 - fall_out: 0.0042 - mcc: 0.2229 - val_loss: 1.7587 - val_accuracy: 0.3700 - val_recall: 0.0850 - val_precision: 0.8095 - val_AUROC: 0.8295 - val_AUPRC: 0.3886 - val_f1_score: 0.1538 - val_balanced_accuracy: 0.5414 - val_specificity: 0.9978 - val_miss_rate: 0.9150 - val_fall_out: 0.0022 - val_mcc: 0.2436
Epoch 13/100
7/7 [==============================] - 0s 59ms/step - loss: 1.8037 - accuracy: 0.3242 - recall: 0.1176 - precision: 0.6812 - AUROC: 0.7979 - AUPRC: 0.3528 - f1_score: 0.2006 - balanced_accuracy: 0.5558 - specificity: 0.9939 - miss_rate: 0.8824 - fall_out: 0.0061 - mcc: 0.2568 - val_loss: 1.7785 - val_accuracy: 0.3650 - val_recall: 0.0850 - val_precision: 0.8095 - val_AUROC: 0.8227 - val_AUPRC: 0.3890 - val_f1_score: 0.1538 - val_balanced_accuracy: 0.5414 - val_specificity: 0.9978 - val_miss_rate: 0.9150 - val_fall_out: 0.0022 - val_mcc: 0.2436
Epoch 14/100
7/7 [==============================] - 0s 59ms/step - loss: 1.7408 - accuracy: 0.3579 - recall: 0.1314 - precision: 0.7554 - AUROC: 0.8157 - AUPRC: 0.3933 - f1_score: 0.2239 - balanced_accuracy: 0.5633 - specificity: 0.9953 - miss_rate: 0.8686 - fall_out: 0.0047 - mcc: 0.2907 - val_loss: 1.7065 - val_accuracy: 0.3700 - val_recall: 0.1300 - val_precision: 0.7429 - val_AUROC: 0.8359 - val_AUPRC: 0.4135 - val_f1_score: 0.2213 - val_balanced_accuracy: 0.5625 - val_specificity: 0.9950 - val_miss_rate: 0.8700 - val_fall_out: 0.0050 - val_mcc: 0.2860
Epoch 15/100
7/7 [==============================] - 0s 59ms/step - loss: 1.6992 - accuracy: 0.3767 - recall: 0.1464 - precision: 0.7800 - AUROC: 0.8244 - AUPRC: 0.4142 - f1_score: 0.2466 - balanced_accuracy: 0.5709 - specificity: 0.9954 - miss_rate: 0.8536 - fall_out: 0.0046 - mcc: 0.3135 - val_loss: 1.8107 - val_accuracy: 0.3350 - val_recall: 0.1550 - val_precision: 0.5741 - val_AUROC: 0.8055 - val_AUPRC: 0.3700 - val_f1_score: 0.2441 - val_balanced_accuracy: 0.5711 - val_specificity: 0.9872 - val_miss_rate: 0.8450 - val_fall_out: 0.0128 - val_mcc: 0.2632
Epoch 16/100
7/7 [==============================] - 0s 58ms/step - loss: 1.6962 - accuracy: 0.3755 - recall: 0.1489 - precision: 0.6839 - AUROC: 0.8255 - AUPRC: 0.4103 - f1_score: 0.2446 - balanced_accuracy: 0.5706 - specificity: 0.9924 - miss_rate: 0.8511 - fall_out: 0.0076 - mcc: 0.2904 - val_loss: 1.6459 - val_accuracy: 0.4000 - val_recall: 0.1400 - val_precision: 0.6667 - val_AUROC: 0.8454 - val_AUPRC: 0.4142 - val_f1_score: 0.2314 - val_balanced_accuracy: 0.5661 - val_specificity: 0.9922 - val_miss_rate: 0.8600 - val_fall_out: 0.0078 - val_mcc: 0.2766
Epoch 17/100
7/7 [==============================] - 0s 59ms/step - loss: 1.6743 - accuracy: 0.4143 - recall: 0.1715 - precision: 0.6650 - AUROC: 0.8299 - AUPRC: 0.4319 - f1_score: 0.2726 - balanced_accuracy: 0.5809 - specificity: 0.9904 - miss_rate: 0.8285 - fall_out: 0.0096 - mcc: 0.3064 - val_loss: 1.6571 - val_accuracy: 0.3900 - val_recall: 0.1050 - val_precision: 0.9130 - val_AUROC: 0.8451 - val_AUPRC: 0.4377 - val_f1_score: 0.1883 - val_balanced_accuracy: 0.5519 - val_specificity: 0.9989 - val_miss_rate: 0.8950 - val_fall_out: 0.0011 - val_mcc: 0.2923
Epoch 18/100
7/7 [==============================] - 0s 59ms/step - loss: 1.6408 - accuracy: 0.3980 - recall: 0.1402 - precision: 0.7832 - AUROC: 0.8414 - AUPRC: 0.4311 - f1_score: 0.2378 - balanced_accuracy: 0.5679 - specificity: 0.9957 - miss_rate: 0.8598 - fall_out: 0.0043 - mcc: 0.3074 - val_loss: 1.6019 - val_accuracy: 0.4050 - val_recall: 0.1550 - val_precision: 0.6596 - val_AUROC: 0.8568 - val_AUPRC: 0.4417 - val_f1_score: 0.2510 - val_balanced_accuracy: 0.5731 - val_specificity: 0.9911 - val_miss_rate: 0.8450 - val_fall_out: 0.0089 - val_mcc: 0.2894
Epoch 19/100
7/7 [==============================] - 0s 59ms/step - loss: 1.6421 - accuracy: 0.4030 - recall: 0.1577 - precision: 0.7368 - AUROC: 0.8381 - AUPRC: 0.4269 - f1_score: 0.2598 - balanced_accuracy: 0.5757 - specificity: 0.9937 - miss_rate: 0.8423 - fall_out: 0.0063 - mcc: 0.3139 - val_loss: 1.5938 - val_accuracy: 0.4150 - val_recall: 0.1500 - val_precision: 0.7143 - val_AUROC: 0.8532 - val_AUPRC: 0.4469 - val_f1_score: 0.2479 - val_balanced_accuracy: 0.5717 - val_specificity: 0.9933 - val_miss_rate: 0.8500 - val_fall_out: 0.0067 - val_mcc: 0.2999
Epoch 20/100
7/7 [==============================] - 0s 61ms/step - loss: 1.6079 - accuracy: 0.4243 - recall: 0.1952 - precision: 0.6724 - AUROC: 0.8466 - AUPRC: 0.4470 - f1_score: 0.3026 - balanced_accuracy: 0.5923 - specificity: 0.9894 - miss_rate: 0.8048 - fall_out: 0.0106 - mcc: 0.3300 - val_loss: 1.5951 - val_accuracy: 0.4200 - val_recall: 0.1250 - val_precision: 0.7143 - val_AUROC: 0.8553 - val_AUPRC: 0.4484 - val_f1_score: 0.2128 - val_balanced_accuracy: 0.5597 - val_specificity: 0.9944 - val_miss_rate: 0.8750 - val_fall_out: 0.0056 - val_mcc: 0.2733
Epoch 21/100
7/7 [==============================] - 0s 59ms/step - loss: 1.5401 - accuracy: 0.4318 - recall: 0.1890 - precision: 0.7626 - AUROC: 0.8612 - AUPRC: 0.4779 - f1_score: 0.3029 - balanced_accuracy: 0.5912 - specificity: 0.9935 - miss_rate: 0.8110 - fall_out: 0.0065 - mcc: 0.3521 - val_loss: 1.5341 - val_accuracy: 0.4050 - val_recall: 0.1850 - val_precision: 0.7115 - val_AUROC: 0.8687 - val_AUPRC: 0.4744 - val_f1_score: 0.2937 - val_balanced_accuracy: 0.5883 - val_specificity: 0.9917 - val_miss_rate: 0.8150 - val_fall_out: 0.0083 - val_mcc: 0.3331
Epoch 22/100
7/7 [==============================] - 0s 59ms/step - loss: 1.5171 - accuracy: 0.4506 - recall: 0.2303 - precision: 0.7510 - AUROC: 0.8646 - AUPRC: 0.4993 - f1_score: 0.3525 - balanced_accuracy: 0.6109 - specificity: 0.9915 - miss_rate: 0.7697 - fall_out: 0.0085 - mcc: 0.3860 - val_loss: 1.4939 - val_accuracy: 0.4450 - val_recall: 0.1850 - val_precision: 0.7255 - val_AUROC: 0.8755 - val_AUPRC: 0.4911 - val_f1_score: 0.2948 - val_balanced_accuracy: 0.5886 - val_specificity: 0.9922 - val_miss_rate: 0.8150 - val_fall_out: 0.0078 - val_mcc: 0.3373
Epoch 23/100
7/7 [==============================] - 0s 59ms/step - loss: 1.5515 - accuracy: 0.4556 - recall: 0.2240 - precision: 0.7131 - AUROC: 0.8568 - AUPRC: 0.4882 - f1_score: 0.3410 - balanced_accuracy: 0.6070 - specificity: 0.9900 - miss_rate: 0.7760 - fall_out: 0.0100 - mcc: 0.3681 - val_loss: 1.5369 - val_accuracy: 0.4100 - val_recall: 0.1550 - val_precision: 0.6739 - val_AUROC: 0.8670 - val_AUPRC: 0.4697 - val_f1_score: 0.2520 - val_balanced_accuracy: 0.5733 - val_specificity: 0.9917 - val_miss_rate: 0.8450 - val_fall_out: 0.0083 - val_mcc: 0.2935
Epoch 24/100
7/7 [==============================] - 0s 59ms/step - loss: 1.5641 - accuracy: 0.4305 - recall: 0.1990 - precision: 0.7756 - AUROC: 0.8567 - AUPRC: 0.4894 - f1_score: 0.3167 - balanced_accuracy: 0.5963 - specificity: 0.9936 - miss_rate: 0.8010 - fall_out: 0.0064 - mcc: 0.3654 - val_loss: 1.5485 - val_accuracy: 0.4150 - val_recall: 0.1600 - val_precision: 0.6957 - val_AUROC: 0.8682 - val_AUPRC: 0.4789 - val_f1_score: 0.2602 - val_balanced_accuracy: 0.5761 - val_specificity: 0.9922 - val_miss_rate: 0.8400 - val_fall_out: 0.0078 - val_mcc: 0.3046
25/25 [==============================] - 0s 8ms/step - loss: 1.4102 - accuracy: 0.5419 - recall: 0.1915 - precision: 0.8270 - AUROC: 0.8940 - AUPRC: 0.5764 - f1_score: 0.3110 - balanced_accuracy: 0.5935 - specificity: 0.9955 - miss_rate: 0.8085 - fall_out: 0.0045 - mcc: 0.3731
7/7 [==============================] - 0s 8ms/step - loss: 1.5485 - accuracy: 0.4150 - recall: 0.1600 - precision: 0.6957 - AUROC: 0.8682 - AUPRC: 0.4789 - f1_score: 0.2602 - balanced_accuracy: 0.5761 - specificity: 0.9922 - miss_rate: 0.8400 - fall_out: 0.0078 - mcc: 0.3046
5it [01:01, 12.42s/it]
-- HOLDOUT 6
Model: "CNN_MelS_30s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_140 (Conv2D) (None, 100, 100, 128) 1280
max_pooling2d_140 (MaxPooli (None, 50, 50, 128) 0
ng2D)
conv2d_141 (Conv2D) (None, 50, 50, 64) 73792
max_pooling2d_141 (MaxPooli (None, 25, 25, 64) 0
ng2D)
conv2d_142 (Conv2D) (None, 25, 25, 64) 36928
max_pooling2d_142 (MaxPooli (None, 13, 13, 64) 0
ng2D)
conv2d_143 (Conv2D) (None, 13, 13, 128) 73856
max_pooling2d_143 (MaxPooli (None, 7, 7, 128) 0
ng2D)
flatten_35 (Flatten) (None, 6272) 0
dense_74 (Dense) (None, 128) 802944
dropout_37 (Dropout) (None, 128) 0
dense_75 (Dense) (None, 10) 1290
=================================================================
Total params: 990,090
Trainable params: 990,090
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
7/7 [==============================] - 3s 147ms/step - loss: 2.3172 - accuracy: 0.1001 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.4887 - AUPRC: 0.0950 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.3026 - val_accuracy: 0.1700 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5246 - val_AUPRC: 0.1220 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 2/100
7/7 [==============================] - 0s 59ms/step - loss: 2.3068 - accuracy: 0.1026 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.5049 - AUPRC: 0.0999 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.3008 - val_accuracy: 0.1000 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5120 - val_AUPRC: 0.1037 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 3/100
7/7 [==============================] - 0s 59ms/step - loss: 2.3020 - accuracy: 0.1189 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.5109 - AUPRC: 0.1076 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.3000 - val_accuracy: 0.1400 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5378 - val_AUPRC: 0.1115 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 4/100
7/7 [==============================] - 0s 59ms/step - loss: 2.2973 - accuracy: 0.1352 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.5382 - AUPRC: 0.1178 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.2895 - val_accuracy: 0.1400 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5901 - val_AUPRC: 0.1713 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 5/100
7/7 [==============================] - 0s 59ms/step - loss: 2.2755 - accuracy: 0.1464 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.5812 - AUPRC: 0.1328 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.2316 - val_accuracy: 0.1950 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6232 - val_AUPRC: 0.1848 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 6/100
7/7 [==============================] - 0s 59ms/step - loss: 2.2071 - accuracy: 0.1877 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.6396 - AUPRC: 0.1614 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.1218 - val_accuracy: 0.2650 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7194 - val_AUPRC: 0.2788 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 7/100
7/7 [==============================] - 0s 59ms/step - loss: 2.1377 - accuracy: 0.2053 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.6725 - AUPRC: 0.1849 - f1_score: 0.0000e+00 - balanced_accuracy: 0.4999 - specificity: 0.9997 - miss_rate: 1.0000 - fall_out: 2.7813e-04 - mcc: -0.0053 - val_loss: 2.0548 - val_accuracy: 0.2750 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7229 - val_AUPRC: 0.2685 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 8/100
7/7 [==============================] - 0s 59ms/step - loss: 2.0800 - accuracy: 0.2178 - recall: 0.0138 - precision: 0.4583 - AUROC: 0.6988 - AUPRC: 0.2100 - f1_score: 0.0267 - balanced_accuracy: 0.5060 - specificity: 0.9982 - miss_rate: 0.9862 - fall_out: 0.0018 - mcc: 0.0656 - val_loss: 1.9966 - val_accuracy: 0.2750 - val_recall: 0.0050 - val_precision: 0.5000 - val_AUROC: 0.7282 - val_AUPRC: 0.2697 - val_f1_score: 0.0099 - val_balanced_accuracy: 0.5022 - val_specificity: 0.9994 - val_miss_rate: 0.9950 - val_fall_out: 5.5556e-04 - val_mcc: 0.0422
Epoch 9/100
7/7 [==============================] - 0s 59ms/step - loss: 2.0050 - accuracy: 0.2390 - recall: 0.0225 - precision: 0.4286 - AUROC: 0.7341 - AUPRC: 0.2366 - f1_score: 0.0428 - balanced_accuracy: 0.5096 - specificity: 0.9967 - miss_rate: 0.9775 - fall_out: 0.0033 - mcc: 0.0796 - val_loss: 1.9266 - val_accuracy: 0.3100 - val_recall: 0.0100 - val_precision: 0.6667 - val_AUROC: 0.7586 - val_AUPRC: 0.3256 - val_f1_score: 0.0197 - val_balanced_accuracy: 0.5047 - val_specificity: 0.9994 - val_miss_rate: 0.9900 - val_fall_out: 5.5556e-04 - val_mcc: 0.0732
Epoch 10/100
7/7 [==============================] - 0s 59ms/step - loss: 1.9578 - accuracy: 0.2766 - recall: 0.0526 - precision: 0.6562 - AUROC: 0.7483 - AUPRC: 0.2793 - f1_score: 0.0973 - balanced_accuracy: 0.5248 - specificity: 0.9969 - miss_rate: 0.9474 - fall_out: 0.0031 - mcc: 0.1666 - val_loss: 1.8959 - val_accuracy: 0.3550 - val_recall: 0.0200 - val_precision: 1.0000 - val_AUROC: 0.7765 - val_AUPRC: 0.3696 - val_f1_score: 0.0392 - val_balanced_accuracy: 0.5100 - val_specificity: 1.0000 - val_miss_rate: 0.9800 - val_fall_out: 0.0000e+00 - val_mcc: 0.1343
Epoch 11/100
7/7 [==============================] - 0s 59ms/step - loss: 1.9218 - accuracy: 0.2991 - recall: 0.0638 - precision: 0.7183 - AUROC: 0.7554 - AUPRC: 0.2987 - f1_score: 0.1172 - balanced_accuracy: 0.5305 - specificity: 0.9972 - miss_rate: 0.9362 - fall_out: 0.0028 - mcc: 0.1952 - val_loss: 1.9172 - val_accuracy: 0.2900 - val_recall: 0.1400 - val_precision: 0.6829 - val_AUROC: 0.7573 - val_AUPRC: 0.3124 - val_f1_score: 0.2324 - val_balanced_accuracy: 0.5664 - val_specificity: 0.9928 - val_miss_rate: 0.8600 - val_fall_out: 0.0072 - val_mcc: 0.2811
Epoch 12/100
7/7 [==============================] - 0s 60ms/step - loss: 1.8999 - accuracy: 0.3029 - recall: 0.0939 - precision: 0.6198 - AUROC: 0.7675 - AUPRC: 0.3089 - f1_score: 0.1630 - balanced_accuracy: 0.5437 - specificity: 0.9936 - miss_rate: 0.9061 - fall_out: 0.0064 - mcc: 0.2149 - val_loss: 1.9236 - val_accuracy: 0.3100 - val_recall: 0.0700 - val_precision: 0.9333 - val_AUROC: 0.7609 - val_AUPRC: 0.3337 - val_f1_score: 0.1302 - val_balanced_accuracy: 0.5347 - val_specificity: 0.9994 - val_miss_rate: 0.9300 - val_fall_out: 5.5556e-04 - val_mcc: 0.2415
25/25 [==============================] - 0s 8ms/step - loss: 1.8323 - accuracy: 0.3479 - recall: 0.0738 - precision: 0.8676 - AUROC: 0.7931 - AUPRC: 0.3735 - f1_score: 0.1361 - balanced_accuracy: 0.5363 - specificity: 0.9987 - miss_rate: 0.9262 - fall_out: 0.0013 - mcc: 0.2371
7/7 [==============================] - 0s 8ms/step - loss: 1.9236 - accuracy: 0.3100 - recall: 0.0700 - precision: 0.9333 - AUROC: 0.7609 - AUPRC: 0.3337 - f1_score: 0.1302 - balanced_accuracy: 0.5347 - specificity: 0.9994 - miss_rate: 0.9300 - fall_out: 5.5556e-04 - mcc: 0.2415
6it [01:09, 10.98s/it]
-- HOLDOUT 7
Model: "CNN_MelS_30s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_144 (Conv2D) (None, 100, 100, 128) 1280
max_pooling2d_144 (MaxPooli (None, 50, 50, 128) 0
ng2D)
conv2d_145 (Conv2D) (None, 50, 50, 64) 73792
max_pooling2d_145 (MaxPooli (None, 25, 25, 64) 0
ng2D)
conv2d_146 (Conv2D) (None, 25, 25, 64) 36928
max_pooling2d_146 (MaxPooli (None, 13, 13, 64) 0
ng2D)
conv2d_147 (Conv2D) (None, 13, 13, 128) 73856
max_pooling2d_147 (MaxPooli (None, 7, 7, 128) 0
ng2D)
flatten_36 (Flatten) (None, 6272) 0
dense_76 (Dense) (None, 128) 802944
dropout_38 (Dropout) (None, 128) 0
dense_77 (Dense) (None, 10) 1290
=================================================================
Total params: 990,090
Trainable params: 990,090
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
7/7 [==============================] - 2s 133ms/step - loss: 2.3133 - accuracy: 0.1026 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.4964 - AUPRC: 0.1005 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.3015 - val_accuracy: 0.1000 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5050 - val_AUPRC: 0.1014 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 2/100
7/7 [==============================] - 0s 59ms/step - loss: 2.3028 - accuracy: 0.1089 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.5097 - AUPRC: 0.1031 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.2984 - val_accuracy: 0.1000 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5411 - val_AUPRC: 0.1158 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 3/100
7/7 [==============================] - 0s 59ms/step - loss: 2.2927 - accuracy: 0.1151 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.5435 - AUPRC: 0.1193 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.2726 - val_accuracy: 0.0950 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5882 - val_AUPRC: 0.1304 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 4/100
7/7 [==============================] - 0s 59ms/step - loss: 2.2482 - accuracy: 0.1464 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.6022 - AUPRC: 0.1381 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.2241 - val_accuracy: 0.1400 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6066 - val_AUPRC: 0.1395 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 5/100
7/7 [==============================] - 0s 59ms/step - loss: 2.2226 - accuracy: 0.1602 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.6260 - AUPRC: 0.1447 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.1299 - val_accuracy: 0.2650 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7079 - val_AUPRC: 0.2287 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 6/100
7/7 [==============================] - 0s 59ms/step - loss: 2.1350 - accuracy: 0.2115 - recall: 0.0050 - precision: 1.0000 - AUROC: 0.6778 - AUPRC: 0.1904 - f1_score: 0.0100 - balanced_accuracy: 0.5025 - specificity: 1.0000 - miss_rate: 0.9950 - fall_out: 0.0000e+00 - mcc: 0.0671 - val_loss: 2.0023 - val_accuracy: 0.2600 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7375 - val_AUPRC: 0.2782 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 7/100
7/7 [==============================] - 0s 59ms/step - loss: 2.0443 - accuracy: 0.2503 - recall: 0.0413 - precision: 0.7500 - AUROC: 0.7096 - AUPRC: 0.2562 - f1_score: 0.0783 - balanced_accuracy: 0.5199 - specificity: 0.9985 - miss_rate: 0.9587 - fall_out: 0.0015 - mcc: 0.1612 - val_loss: 1.9351 - val_accuracy: 0.2950 - val_recall: 0.0500 - val_precision: 0.8333 - val_AUROC: 0.7703 - val_AUPRC: 0.2981 - val_f1_score: 0.0943 - val_balanced_accuracy: 0.5244 - val_specificity: 0.9989 - val_miss_rate: 0.9500 - val_fall_out: 0.0011 - val_mcc: 0.1899
Epoch 8/100
7/7 [==============================] - 0s 59ms/step - loss: 1.9840 - accuracy: 0.2716 - recall: 0.0363 - precision: 0.6170 - AUROC: 0.7418 - AUPRC: 0.2618 - f1_score: 0.0686 - balanced_accuracy: 0.5169 - specificity: 0.9975 - miss_rate: 0.9637 - fall_out: 0.0025 - mcc: 0.1326 - val_loss: 1.8813 - val_accuracy: 0.3150 - val_recall: 0.0600 - val_precision: 0.8571 - val_AUROC: 0.7774 - val_AUPRC: 0.3387 - val_f1_score: 0.1121 - val_balanced_accuracy: 0.5294 - val_specificity: 0.9989 - val_miss_rate: 0.9400 - val_fall_out: 0.0011 - val_mcc: 0.2119
Epoch 9/100
7/7 [==============================] - 0s 59ms/step - loss: 1.9873 - accuracy: 0.3066 - recall: 0.0313 - precision: 0.5682 - AUROC: 0.7416 - AUPRC: 0.2693 - f1_score: 0.0593 - balanced_accuracy: 0.5143 - specificity: 0.9974 - miss_rate: 0.9687 - fall_out: 0.0026 - mcc: 0.1161 - val_loss: 1.9365 - val_accuracy: 0.2550 - val_recall: 0.0650 - val_precision: 0.7647 - val_AUROC: 0.7685 - val_AUPRC: 0.2819 - val_f1_score: 0.1198 - val_balanced_accuracy: 0.5314 - val_specificity: 0.9978 - val_miss_rate: 0.9350 - val_fall_out: 0.0022 - val_mcc: 0.2051
Epoch 10/100
7/7 [==============================] - 0s 59ms/step - loss: 1.8978 - accuracy: 0.2941 - recall: 0.0588 - precision: 0.6267 - AUROC: 0.7764 - AUPRC: 0.2974 - f1_score: 0.1076 - balanced_accuracy: 0.5275 - specificity: 0.9961 - miss_rate: 0.9412 - fall_out: 0.0039 - mcc: 0.1709 - val_loss: 1.7658 - val_accuracy: 0.3250 - val_recall: 0.1250 - val_precision: 0.7353 - val_AUROC: 0.8068 - val_AUPRC: 0.3792 - val_f1_score: 0.2137 - val_balanced_accuracy: 0.5600 - val_specificity: 0.9950 - val_miss_rate: 0.8750 - val_fall_out: 0.0050 - val_mcc: 0.2785
Epoch 11/100
7/7 [==============================] - 0s 59ms/step - loss: 1.8536 - accuracy: 0.3317 - recall: 0.0976 - precision: 0.6446 - AUROC: 0.7845 - AUPRC: 0.3400 - f1_score: 0.1696 - balanced_accuracy: 0.5458 - specificity: 0.9940 - miss_rate: 0.9024 - fall_out: 0.0060 - mcc: 0.2251 - val_loss: 1.6929 - val_accuracy: 0.3950 - val_recall: 0.0700 - val_precision: 0.8750 - val_AUROC: 0.8494 - val_AUPRC: 0.4330 - val_f1_score: 0.1296 - val_balanced_accuracy: 0.5344 - val_specificity: 0.9989 - val_miss_rate: 0.9300 - val_fall_out: 0.0011 - val_mcc: 0.2320
Epoch 12/100
7/7 [==============================] - 0s 59ms/step - loss: 1.7776 - accuracy: 0.3504 - recall: 0.0989 - precision: 0.6810 - AUROC: 0.8066 - AUPRC: 0.3724 - f1_score: 0.1727 - balanced_accuracy: 0.5469 - specificity: 0.9949 - miss_rate: 0.9011 - fall_out: 0.0051 - mcc: 0.2351 - val_loss: 1.6642 - val_accuracy: 0.4050 - val_recall: 0.1350 - val_precision: 0.9000 - val_AUROC: 0.8431 - val_AUPRC: 0.4448 - val_f1_score: 0.2348 - val_balanced_accuracy: 0.5667 - val_specificity: 0.9983 - val_miss_rate: 0.8650 - val_fall_out: 0.0017 - val_mcc: 0.3291
Epoch 13/100
7/7 [==============================] - 0s 59ms/step - loss: 1.7775 - accuracy: 0.3304 - recall: 0.0851 - precision: 0.7234 - AUROC: 0.8064 - AUPRC: 0.3703 - f1_score: 0.1523 - balanced_accuracy: 0.5407 - specificity: 0.9964 - miss_rate: 0.9149 - fall_out: 0.0036 - mcc: 0.2267 - val_loss: 1.6056 - val_accuracy: 0.4450 - val_recall: 0.1250 - val_precision: 0.7576 - val_AUROC: 0.8562 - val_AUPRC: 0.4769 - val_f1_score: 0.2146 - val_balanced_accuracy: 0.5603 - val_specificity: 0.9956 - val_miss_rate: 0.8750 - val_fall_out: 0.0044 - val_mcc: 0.2839
Epoch 14/100
7/7 [==============================] - 0s 59ms/step - loss: 1.7245 - accuracy: 0.3730 - recall: 0.1239 - precision: 0.7500 - AUROC: 0.8171 - AUPRC: 0.3975 - f1_score: 0.2127 - balanced_accuracy: 0.5597 - specificity: 0.9954 - miss_rate: 0.8761 - fall_out: 0.0046 - mcc: 0.2808 - val_loss: 1.5413 - val_accuracy: 0.4450 - val_recall: 0.1250 - val_precision: 0.8333 - val_AUROC: 0.8718 - val_AUPRC: 0.4830 - val_f1_score: 0.2174 - val_balanced_accuracy: 0.5611 - val_specificity: 0.9972 - val_miss_rate: 0.8750 - val_fall_out: 0.0028 - val_mcc: 0.3017
Epoch 15/100
7/7 [==============================] - 0s 59ms/step - loss: 1.6767 - accuracy: 0.3905 - recall: 0.1627 - precision: 0.6842 - AUROC: 0.8311 - AUPRC: 0.4197 - f1_score: 0.2629 - balanced_accuracy: 0.5772 - specificity: 0.9917 - miss_rate: 0.8373 - fall_out: 0.0083 - mcc: 0.3039 - val_loss: 1.5904 - val_accuracy: 0.4000 - val_recall: 0.1600 - val_precision: 0.7805 - val_AUROC: 0.8521 - val_AUPRC: 0.4608 - val_f1_score: 0.2656 - val_balanced_accuracy: 0.5775 - val_specificity: 0.9950 - val_miss_rate: 0.8400 - val_fall_out: 0.0050 - val_mcc: 0.3282
Epoch 16/100
7/7 [==============================] - 0s 60ms/step - loss: 1.6567 - accuracy: 0.3855 - recall: 0.1640 - precision: 0.7939 - AUROC: 0.8363 - AUPRC: 0.4357 - f1_score: 0.2718 - balanced_accuracy: 0.5796 - specificity: 0.9953 - miss_rate: 0.8360 - fall_out: 0.0047 - mcc: 0.3359 - val_loss: 1.5141 - val_accuracy: 0.4250 - val_recall: 0.1850 - val_precision: 0.7255 - val_AUROC: 0.8726 - val_AUPRC: 0.4903 - val_f1_score: 0.2948 - val_balanced_accuracy: 0.5886 - val_specificity: 0.9922 - val_miss_rate: 0.8150 - val_fall_out: 0.0078 - val_mcc: 0.3373
Epoch 17/100
7/7 [==============================] - 0s 59ms/step - loss: 1.6127 - accuracy: 0.4280 - recall: 0.1727 - precision: 0.7113 - AUROC: 0.8462 - AUPRC: 0.4560 - f1_score: 0.2779 - balanced_accuracy: 0.5825 - specificity: 0.9922 - miss_rate: 0.8273 - fall_out: 0.0078 - mcc: 0.3215 - val_loss: 1.5183 - val_accuracy: 0.4350 - val_recall: 0.1950 - val_precision: 0.6964 - val_AUROC: 0.8691 - val_AUPRC: 0.4916 - val_f1_score: 0.3047 - val_balanced_accuracy: 0.5928 - val_specificity: 0.9906 - val_miss_rate: 0.8050 - val_fall_out: 0.0094 - val_mcc: 0.3374
Epoch 18/100
7/7 [==============================] - 0s 59ms/step - loss: 1.5610 - accuracy: 0.4418 - recall: 0.1965 - precision: 0.7548 - AUROC: 0.8563 - AUPRC: 0.4892 - f1_score: 0.3118 - balanced_accuracy: 0.5947 - specificity: 0.9929 - miss_rate: 0.8035 - fall_out: 0.0071 - mcc: 0.3568 - val_loss: 1.4721 - val_accuracy: 0.4650 - val_recall: 0.1750 - val_precision: 0.7778 - val_AUROC: 0.8759 - val_AUPRC: 0.5209 - val_f1_score: 0.2857 - val_balanced_accuracy: 0.5847 - val_specificity: 0.9944 - val_miss_rate: 0.8250 - val_fall_out: 0.0056 - val_mcc: 0.3428
Epoch 19/100
7/7 [==============================] - 0s 59ms/step - loss: 1.5608 - accuracy: 0.4643 - recall: 0.2078 - precision: 0.7378 - AUROC: 0.8553 - AUPRC: 0.4850 - f1_score: 0.3242 - balanced_accuracy: 0.5998 - specificity: 0.9918 - miss_rate: 0.7922 - fall_out: 0.0082 - mcc: 0.3619 - val_loss: 1.4736 - val_accuracy: 0.4250 - val_recall: 0.2050 - val_precision: 0.7593 - val_AUROC: 0.8750 - val_AUPRC: 0.5064 - val_f1_score: 0.3228 - val_balanced_accuracy: 0.5989 - val_specificity: 0.9928 - val_miss_rate: 0.7950 - val_fall_out: 0.0072 - val_mcc: 0.3661
Epoch 20/100
7/7 [==============================] - 0s 60ms/step - loss: 1.6018 - accuracy: 0.4230 - recall: 0.2128 - precision: 0.6939 - AUROC: 0.8482 - AUPRC: 0.4690 - f1_score: 0.3257 - balanced_accuracy: 0.6012 - specificity: 0.9896 - miss_rate: 0.7872 - fall_out: 0.0104 - mcc: 0.3521 - val_loss: 1.4690 - val_accuracy: 0.4950 - val_recall: 0.1700 - val_precision: 0.8500 - val_AUROC: 0.8832 - val_AUPRC: 0.5272 - val_f1_score: 0.2833 - val_balanced_accuracy: 0.5833 - val_specificity: 0.9967 - val_miss_rate: 0.8300 - val_fall_out: 0.0033 - val_mcc: 0.3571
Epoch 21/100
7/7 [==============================] - 0s 59ms/step - loss: 1.5856 - accuracy: 0.4280 - recall: 0.1677 - precision: 0.7746 - AUROC: 0.8539 - AUPRC: 0.4794 - f1_score: 0.2757 - balanced_accuracy: 0.5811 - specificity: 0.9946 - miss_rate: 0.8323 - fall_out: 0.0054 - mcc: 0.3345 - val_loss: 1.4705 - val_accuracy: 0.5250 - val_recall: 0.1700 - val_precision: 0.7083 - val_AUROC: 0.8806 - val_AUPRC: 0.5207 - val_f1_score: 0.2742 - val_balanced_accuracy: 0.5811 - val_specificity: 0.9922 - val_miss_rate: 0.8300 - val_fall_out: 0.0078 - val_mcc: 0.3180
Epoch 22/100
7/7 [==============================] - 0s 59ms/step - loss: 1.5725 - accuracy: 0.4456 - recall: 0.2015 - precision: 0.7318 - AUROC: 0.8508 - AUPRC: 0.4823 - f1_score: 0.3160 - balanced_accuracy: 0.5966 - specificity: 0.9918 - miss_rate: 0.7985 - fall_out: 0.0082 - mcc: 0.3544 - val_loss: 1.4348 - val_accuracy: 0.4850 - val_recall: 0.2200 - val_precision: 0.7857 - val_AUROC: 0.8830 - val_AUPRC: 0.5433 - val_f1_score: 0.3438 - val_balanced_accuracy: 0.6067 - val_specificity: 0.9933 - val_miss_rate: 0.7800 - val_fall_out: 0.0067 - val_mcc: 0.3879
Epoch 23/100
7/7 [==============================] - 0s 59ms/step - loss: 1.5364 - accuracy: 0.4631 - recall: 0.2103 - precision: 0.7602 - AUROC: 0.8617 - AUPRC: 0.4994 - f1_score: 0.3294 - balanced_accuracy: 0.6014 - specificity: 0.9926 - miss_rate: 0.7897 - fall_out: 0.0074 - mcc: 0.3712 - val_loss: 1.4232 - val_accuracy: 0.4700 - val_recall: 0.2050 - val_precision: 0.7455 - val_AUROC: 0.8838 - val_AUPRC: 0.5351 - val_f1_score: 0.3216 - val_balanced_accuracy: 0.5986 - val_specificity: 0.9922 - val_miss_rate: 0.7950 - val_fall_out: 0.0078 - val_mcc: 0.3618
Epoch 24/100
7/7 [==============================] - 0s 59ms/step - loss: 1.4577 - accuracy: 0.5069 - recall: 0.2353 - precision: 0.7581 - AUROC: 0.8767 - AUPRC: 0.5303 - f1_score: 0.3591 - balanced_accuracy: 0.6135 - specificity: 0.9917 - miss_rate: 0.7647 - fall_out: 0.0083 - mcc: 0.3926 - val_loss: 1.4647 - val_accuracy: 0.4900 - val_recall: 0.2350 - val_precision: 0.7460 - val_AUROC: 0.8746 - val_AUPRC: 0.5329 - val_f1_score: 0.3574 - val_balanced_accuracy: 0.6131 - val_specificity: 0.9911 - val_miss_rate: 0.7650 - val_fall_out: 0.0089 - val_mcc: 0.3884
Epoch 25/100
7/7 [==============================] - 0s 59ms/step - loss: 1.4689 - accuracy: 0.4706 - recall: 0.2365 - precision: 0.7530 - AUROC: 0.8734 - AUPRC: 0.5308 - f1_score: 0.3600 - balanced_accuracy: 0.6140 - specificity: 0.9914 - miss_rate: 0.7635 - fall_out: 0.0086 - mcc: 0.3920 - val_loss: 1.3724 - val_accuracy: 0.4800 - val_recall: 0.2400 - val_precision: 0.7273 - val_AUROC: 0.8939 - val_AUPRC: 0.5488 - val_f1_score: 0.3609 - val_balanced_accuracy: 0.6150 - val_specificity: 0.9900 - val_miss_rate: 0.7600 - val_fall_out: 0.0100 - val_mcc: 0.3863
Epoch 26/100
7/7 [==============================] - 0s 59ms/step - loss: 1.3844 - accuracy: 0.5169 - recall: 0.2766 - precision: 0.7809 - AUROC: 0.8883 - AUPRC: 0.5711 - f1_score: 0.4085 - balanced_accuracy: 0.6340 - specificity: 0.9914 - miss_rate: 0.7234 - fall_out: 0.0086 - mcc: 0.4349 - val_loss: 1.3326 - val_accuracy: 0.5250 - val_recall: 0.2550 - val_precision: 0.8500 - val_AUROC: 0.9011 - val_AUPRC: 0.5829 - val_f1_score: 0.3923 - val_balanced_accuracy: 0.6250 - val_specificity: 0.9950 - val_miss_rate: 0.7450 - val_fall_out: 0.0050 - val_mcc: 0.4397
Epoch 27/100
7/7 [==============================] - 0s 59ms/step - loss: 1.4109 - accuracy: 0.4781 - recall: 0.2466 - precision: 0.7817 - AUROC: 0.8859 - AUPRC: 0.5494 - f1_score: 0.3749 - balanced_accuracy: 0.6195 - specificity: 0.9924 - miss_rate: 0.7534 - fall_out: 0.0076 - mcc: 0.4101 - val_loss: 1.4071 - val_accuracy: 0.4500 - val_recall: 0.2500 - val_precision: 0.7042 - val_AUROC: 0.8862 - val_AUPRC: 0.5310 - val_f1_score: 0.3690 - val_balanced_accuracy: 0.6192 - val_specificity: 0.9883 - val_miss_rate: 0.7500 - val_fall_out: 0.0117 - val_mcc: 0.3864
Epoch 28/100
7/7 [==============================] - 0s 60ms/step - loss: 1.3634 - accuracy: 0.5119 - recall: 0.2778 - precision: 0.7551 - AUROC: 0.8928 - AUPRC: 0.5698 - f1_score: 0.4062 - balanced_accuracy: 0.6339 - specificity: 0.9900 - miss_rate: 0.7222 - fall_out: 0.0100 - mcc: 0.4268 - val_loss: 1.3434 - val_accuracy: 0.5300 - val_recall: 0.2700 - val_precision: 0.7826 - val_AUROC: 0.8979 - val_AUPRC: 0.5772 - val_f1_score: 0.4015 - val_balanced_accuracy: 0.6308 - val_specificity: 0.9917 - val_miss_rate: 0.7300 - val_fall_out: 0.0083 - val_mcc: 0.4301
25/25 [==============================] - 0s 8ms/step - loss: 1.1761 - accuracy: 0.6008 - recall: 0.3129 - precision: 0.8591 - AUROC: 0.9251 - AUPRC: 0.6701 - f1_score: 0.4587 - balanced_accuracy: 0.6536 - specificity: 0.9943 - miss_rate: 0.6871 - fall_out: 0.0057 - mcc: 0.4919
7/7 [==============================] - 0s 8ms/step - loss: 1.3434 - accuracy: 0.5300 - recall: 0.2700 - precision: 0.7826 - AUROC: 0.8979 - AUPRC: 0.5772 - f1_score: 0.4015 - balanced_accuracy: 0.6308 - specificity: 0.9917 - miss_rate: 0.7300 - fall_out: 0.0083 - mcc: 0.4301
7it [01:23, 11.82s/it]
-- HOLDOUT 8
Model: "CNN_MelS_30s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_148 (Conv2D) (None, 100, 100, 128) 1280
max_pooling2d_148 (MaxPooli (None, 50, 50, 128) 0
ng2D)
conv2d_149 (Conv2D) (None, 50, 50, 64) 73792
max_pooling2d_149 (MaxPooli (None, 25, 25, 64) 0
ng2D)
conv2d_150 (Conv2D) (None, 25, 25, 64) 36928
max_pooling2d_150 (MaxPooli (None, 13, 13, 64) 0
ng2D)
conv2d_151 (Conv2D) (None, 13, 13, 128) 73856
max_pooling2d_151 (MaxPooli (None, 7, 7, 128) 0
ng2D)
flatten_37 (Flatten) (None, 6272) 0
dense_78 (Dense) (None, 128) 802944
dropout_39 (Dropout) (None, 128) 0
dense_79 (Dense) (None, 10) 1290
=================================================================
Total params: 990,090
Trainable params: 990,090
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
7/7 [==============================] - 2s 132ms/step - loss: 2.3258 - accuracy: 0.0989 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.4872 - AUPRC: 0.0960 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.3015 - val_accuracy: 0.1000 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.4999 - val_AUPRC: 0.0997 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 2/100
7/7 [==============================] - 0s 59ms/step - loss: 2.3009 - accuracy: 0.1039 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.5120 - AUPRC: 0.1040 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.2965 - val_accuracy: 0.1050 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5365 - val_AUPRC: 0.1209 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 3/100
7/7 [==============================] - 0s 59ms/step - loss: 2.2909 - accuracy: 0.1176 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.5461 - AUPRC: 0.1154 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.2719 - val_accuracy: 0.1700 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6133 - val_AUPRC: 0.1637 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 4/100
7/7 [==============================] - 0s 59ms/step - loss: 2.2588 - accuracy: 0.1464 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.6024 - AUPRC: 0.1360 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.2111 - val_accuracy: 0.2450 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6625 - val_AUPRC: 0.1949 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 5/100
7/7 [==============================] - 0s 59ms/step - loss: 2.1988 - accuracy: 0.1877 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.6444 - AUPRC: 0.1591 - f1_score: 0.0000e+00 - balanced_accuracy: 0.4999 - specificity: 0.9999 - miss_rate: 1.0000 - fall_out: 1.3906e-04 - mcc: -0.0037 - val_loss: 2.1191 - val_accuracy: 0.2350 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7187 - val_AUPRC: 0.2324 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 6/100
7/7 [==============================] - 0s 59ms/step - loss: 2.0930 - accuracy: 0.2203 - recall: 0.0125 - precision: 0.7692 - AUROC: 0.6957 - AUPRC: 0.2196 - f1_score: 0.0246 - balanced_accuracy: 0.5060 - specificity: 0.9996 - miss_rate: 0.9875 - fall_out: 4.1719e-04 - mcc: 0.0901 - val_loss: 2.0070 - val_accuracy: 0.3050 - val_recall: 0.0150 - val_precision: 0.6000 - val_AUROC: 0.7534 - val_AUPRC: 0.2819 - val_f1_score: 0.0293 - val_balanced_accuracy: 0.5069 - val_specificity: 0.9989 - val_miss_rate: 0.9850 - val_fall_out: 0.0011 - val_mcc: 0.0834
Epoch 7/100
7/7 [==============================] - 0s 59ms/step - loss: 2.0571 - accuracy: 0.2441 - recall: 0.0263 - precision: 0.5385 - AUROC: 0.7106 - AUPRC: 0.2288 - f1_score: 0.0501 - balanced_accuracy: 0.5119 - specificity: 0.9975 - miss_rate: 0.9737 - fall_out: 0.0025 - mcc: 0.1024 - val_loss: 1.9978 - val_accuracy: 0.2900 - val_recall: 0.0250 - val_precision: 0.5556 - val_AUROC: 0.7434 - val_AUPRC: 0.2896 - val_f1_score: 0.0478 - val_balanced_accuracy: 0.5114 - val_specificity: 0.9978 - val_miss_rate: 0.9750 - val_fall_out: 0.0022 - val_mcc: 0.1021
Epoch 8/100
7/7 [==============================] - 0s 59ms/step - loss: 1.9757 - accuracy: 0.2566 - recall: 0.0601 - precision: 0.6154 - AUROC: 0.7381 - AUPRC: 0.2733 - f1_score: 0.1095 - balanced_accuracy: 0.5280 - specificity: 0.9958 - miss_rate: 0.9399 - fall_out: 0.0042 - mcc: 0.1706 - val_loss: 1.9261 - val_accuracy: 0.3150 - val_recall: 0.0400 - val_precision: 0.6667 - val_AUROC: 0.7723 - val_AUPRC: 0.3263 - val_f1_score: 0.0755 - val_balanced_accuracy: 0.5189 - val_specificity: 0.9978 - val_miss_rate: 0.9600 - val_fall_out: 0.0022 - val_mcc: 0.1468
Epoch 9/100
7/7 [==============================] - 0s 59ms/step - loss: 1.9813 - accuracy: 0.2603 - recall: 0.0463 - precision: 0.6852 - AUROC: 0.7344 - AUPRC: 0.2750 - f1_score: 0.0868 - balanced_accuracy: 0.5220 - specificity: 0.9976 - miss_rate: 0.9537 - fall_out: 0.0024 - mcc: 0.1609 - val_loss: 1.8893 - val_accuracy: 0.4000 - val_recall: 0.0300 - val_precision: 0.7500 - val_AUROC: 0.7936 - val_AUPRC: 0.3628 - val_f1_score: 0.0577 - val_balanced_accuracy: 0.5144 - val_specificity: 0.9989 - val_miss_rate: 0.9700 - val_fall_out: 0.0011 - val_mcc: 0.1373
Epoch 10/100
7/7 [==============================] - 0s 59ms/step - loss: 1.9105 - accuracy: 0.3191 - recall: 0.0914 - precision: 0.6822 - AUROC: 0.7653 - AUPRC: 0.3280 - f1_score: 0.1611 - balanced_accuracy: 0.5433 - specificity: 0.9953 - miss_rate: 0.9086 - fall_out: 0.0047 - mcc: 0.2261 - val_loss: 1.8865 - val_accuracy: 0.3300 - val_recall: 0.1100 - val_precision: 0.6667 - val_AUROC: 0.7739 - val_AUPRC: 0.3243 - val_f1_score: 0.1888 - val_balanced_accuracy: 0.5519 - val_specificity: 0.9939 - val_miss_rate: 0.8900 - val_fall_out: 0.0061 - val_mcc: 0.2447
Epoch 11/100
7/7 [==============================] - 0s 59ms/step - loss: 1.8591 - accuracy: 0.3141 - recall: 0.1001 - precision: 0.6723 - AUROC: 0.7778 - AUPRC: 0.3388 - f1_score: 0.1743 - balanced_accuracy: 0.5474 - specificity: 0.9946 - miss_rate: 0.8999 - fall_out: 0.0054 - mcc: 0.2346 - val_loss: 1.8366 - val_accuracy: 0.3500 - val_recall: 0.0950 - val_precision: 0.7600 - val_AUROC: 0.8024 - val_AUPRC: 0.3595 - val_f1_score: 0.1689 - val_balanced_accuracy: 0.5458 - val_specificity: 0.9967 - val_miss_rate: 0.9050 - val_fall_out: 0.0033 - val_mcc: 0.2475
Epoch 12/100
7/7 [==============================] - 0s 59ms/step - loss: 1.8632 - accuracy: 0.3404 - recall: 0.0976 - precision: 0.7091 - AUROC: 0.7736 - AUPRC: 0.3382 - f1_score: 0.1716 - balanced_accuracy: 0.5466 - specificity: 0.9955 - miss_rate: 0.9024 - fall_out: 0.0045 - mcc: 0.2399 - val_loss: 1.8697 - val_accuracy: 0.3450 - val_recall: 0.0950 - val_precision: 0.6786 - val_AUROC: 0.7940 - val_AUPRC: 0.3447 - val_f1_score: 0.1667 - val_balanced_accuracy: 0.5450 - val_specificity: 0.9950 - val_miss_rate: 0.9050 - val_fall_out: 0.0050 - val_mcc: 0.2298
Epoch 13/100
7/7 [==============================] - 0s 60ms/step - loss: 1.8394 - accuracy: 0.3179 - recall: 0.1064 - precision: 0.7203 - AUROC: 0.7866 - AUPRC: 0.3488 - f1_score: 0.1854 - balanced_accuracy: 0.5509 - specificity: 0.9954 - miss_rate: 0.8936 - fall_out: 0.0046 - mcc: 0.2532 - val_loss: 1.7518 - val_accuracy: 0.4050 - val_recall: 0.1500 - val_precision: 0.7692 - val_AUROC: 0.8234 - val_AUPRC: 0.4129 - val_f1_score: 0.2510 - val_balanced_accuracy: 0.5725 - val_specificity: 0.9950 - val_miss_rate: 0.8500 - val_fall_out: 0.0050 - val_mcc: 0.3146
Epoch 14/100
7/7 [==============================] - 0s 59ms/step - loss: 1.7698 - accuracy: 0.3429 - recall: 0.1164 - precision: 0.6739 - AUROC: 0.8083 - AUPRC: 0.3720 - f1_score: 0.1985 - balanced_accuracy: 0.5551 - specificity: 0.9937 - miss_rate: 0.8836 - fall_out: 0.0063 - mcc: 0.2536 - val_loss: 1.7061 - val_accuracy: 0.4250 - val_recall: 0.1550 - val_precision: 0.6889 - val_AUROC: 0.8367 - val_AUPRC: 0.4140 - val_f1_score: 0.2531 - val_balanced_accuracy: 0.5736 - val_specificity: 0.9922 - val_miss_rate: 0.8450 - val_fall_out: 0.0078 - val_mcc: 0.2978
Epoch 15/100
7/7 [==============================] - 0s 60ms/step - loss: 1.7557 - accuracy: 0.3667 - recall: 0.1289 - precision: 0.6732 - AUROC: 0.8104 - AUPRC: 0.3844 - f1_score: 0.2164 - balanced_accuracy: 0.5610 - specificity: 0.9930 - miss_rate: 0.8711 - fall_out: 0.0070 - mcc: 0.2670 - val_loss: 1.6833 - val_accuracy: 0.4100 - val_recall: 0.1400 - val_precision: 0.7368 - val_AUROC: 0.8486 - val_AUPRC: 0.4217 - val_f1_score: 0.2353 - val_balanced_accuracy: 0.5672 - val_specificity: 0.9944 - val_miss_rate: 0.8600 - val_fall_out: 0.0056 - val_mcc: 0.2954
Epoch 16/100
7/7 [==============================] - 0s 59ms/step - loss: 1.7036 - accuracy: 0.3880 - recall: 0.1389 - precision: 0.7551 - AUROC: 0.8248 - AUPRC: 0.4139 - f1_score: 0.2347 - balanced_accuracy: 0.5670 - specificity: 0.9950 - miss_rate: 0.8611 - fall_out: 0.0050 - mcc: 0.2990 - val_loss: 1.6529 - val_accuracy: 0.4300 - val_recall: 0.1850 - val_precision: 0.7255 - val_AUROC: 0.8477 - val_AUPRC: 0.4438 - val_f1_score: 0.2948 - val_balanced_accuracy: 0.5886 - val_specificity: 0.9922 - val_miss_rate: 0.8150 - val_fall_out: 0.0078 - val_mcc: 0.3373
Epoch 17/100
7/7 [==============================] - 0s 59ms/step - loss: 1.6505 - accuracy: 0.3917 - recall: 0.1690 - precision: 0.7105 - AUROC: 0.8365 - AUPRC: 0.4295 - f1_score: 0.2730 - balanced_accuracy: 0.5807 - specificity: 0.9924 - miss_rate: 0.8310 - fall_out: 0.0076 - mcc: 0.3176 - val_loss: 1.7029 - val_accuracy: 0.3750 - val_recall: 0.1900 - val_precision: 0.7037 - val_AUROC: 0.8348 - val_AUPRC: 0.4022 - val_f1_score: 0.2992 - val_balanced_accuracy: 0.5906 - val_specificity: 0.9911 - val_miss_rate: 0.8100 - val_fall_out: 0.0089 - val_mcc: 0.3352
Epoch 18/100
7/7 [==============================] - 0s 59ms/step - loss: 1.7963 - accuracy: 0.3554 - recall: 0.1589 - precision: 0.6287 - AUROC: 0.8023 - AUPRC: 0.3756 - f1_score: 0.2537 - balanced_accuracy: 0.5743 - specificity: 0.9896 - miss_rate: 0.8411 - fall_out: 0.0104 - mcc: 0.2838 - val_loss: 1.6439 - val_accuracy: 0.4050 - val_recall: 0.1250 - val_precision: 0.7812 - val_AUROC: 0.8549 - val_AUPRC: 0.4357 - val_f1_score: 0.2155 - val_balanced_accuracy: 0.5606 - val_specificity: 0.9961 - val_miss_rate: 0.8750 - val_fall_out: 0.0039 - val_mcc: 0.2896
Epoch 19/100
7/7 [==============================] - 0s 59ms/step - loss: 1.6811 - accuracy: 0.3917 - recall: 0.1114 - precision: 0.8018 - AUROC: 0.8389 - AUPRC: 0.4299 - f1_score: 0.1956 - balanced_accuracy: 0.5542 - specificity: 0.9969 - miss_rate: 0.8886 - fall_out: 0.0031 - mcc: 0.2777 - val_loss: 1.6059 - val_accuracy: 0.4100 - val_recall: 0.1700 - val_precision: 0.6800 - val_AUROC: 0.8622 - val_AUPRC: 0.4491 - val_f1_score: 0.2720 - val_balanced_accuracy: 0.5806 - val_specificity: 0.9911 - val_miss_rate: 0.8300 - val_fall_out: 0.0089 - val_mcc: 0.3096
Epoch 20/100
7/7 [==============================] - 0s 59ms/step - loss: 1.6469 - accuracy: 0.4080 - recall: 0.1827 - precision: 0.7157 - AUROC: 0.8385 - AUPRC: 0.4396 - f1_score: 0.2911 - balanced_accuracy: 0.5873 - specificity: 0.9919 - miss_rate: 0.8173 - fall_out: 0.0081 - mcc: 0.3322 - val_loss: 1.5341 - val_accuracy: 0.4500 - val_recall: 0.1750 - val_precision: 0.7447 - val_AUROC: 0.8713 - val_AUPRC: 0.4835 - val_f1_score: 0.2834 - val_balanced_accuracy: 0.5842 - val_specificity: 0.9933 - val_miss_rate: 0.8250 - val_fall_out: 0.0067 - val_mcc: 0.3334
Epoch 21/100
7/7 [==============================] - 0s 59ms/step - loss: 1.6861 - accuracy: 0.4055 - recall: 0.1815 - precision: 0.6971 - AUROC: 0.8235 - AUPRC: 0.4271 - f1_score: 0.2880 - balanced_accuracy: 0.5864 - specificity: 0.9912 - miss_rate: 0.8185 - fall_out: 0.0088 - mcc: 0.3254 - val_loss: 1.5854 - val_accuracy: 0.4300 - val_recall: 0.1650 - val_precision: 0.6600 - val_AUROC: 0.8631 - val_AUPRC: 0.4585 - val_f1_score: 0.2640 - val_balanced_accuracy: 0.5778 - val_specificity: 0.9906 - val_miss_rate: 0.8350 - val_fall_out: 0.0094 - val_mcc: 0.2989
Epoch 22/100
7/7 [==============================] - 0s 59ms/step - loss: 1.5727 - accuracy: 0.4393 - recall: 0.1765 - precision: 0.7050 - AUROC: 0.8578 - AUPRC: 0.4606 - f1_score: 0.2823 - balanced_accuracy: 0.5841 - specificity: 0.9918 - miss_rate: 0.8235 - fall_out: 0.0082 - mcc: 0.3231 - val_loss: 1.5023 - val_accuracy: 0.4950 - val_recall: 0.2100 - val_precision: 0.8077 - val_AUROC: 0.8806 - val_AUPRC: 0.4981 - val_f1_score: 0.3333 - val_balanced_accuracy: 0.6022 - val_specificity: 0.9944 - val_miss_rate: 0.7900 - val_fall_out: 0.0056 - val_mcc: 0.3854
Epoch 23/100
7/7 [==============================] - 0s 59ms/step - loss: 1.5503 - accuracy: 0.4318 - recall: 0.2015 - precision: 0.6736 - AUROC: 0.8601 - AUPRC: 0.4785 - f1_score: 0.3102 - balanced_accuracy: 0.5953 - specificity: 0.9892 - miss_rate: 0.7985 - fall_out: 0.0108 - mcc: 0.3358 - val_loss: 1.4627 - val_accuracy: 0.4800 - val_recall: 0.2250 - val_precision: 0.6923 - val_AUROC: 0.8845 - val_AUPRC: 0.5022 - val_f1_score: 0.3396 - val_balanced_accuracy: 0.6069 - val_specificity: 0.9889 - val_miss_rate: 0.7750 - val_fall_out: 0.0111 - val_mcc: 0.3619
Epoch 24/100
7/7 [==============================] - 0s 60ms/step - loss: 1.5602 - accuracy: 0.4380 - recall: 0.2190 - precision: 0.6917 - AUROC: 0.8577 - AUPRC: 0.4728 - f1_score: 0.3327 - balanced_accuracy: 0.6041 - specificity: 0.9892 - miss_rate: 0.7810 - fall_out: 0.0108 - mcc: 0.3567 - val_loss: 1.4855 - val_accuracy: 0.4700 - val_recall: 0.2100 - val_precision: 0.7368 - val_AUROC: 0.8851 - val_AUPRC: 0.5012 - val_f1_score: 0.3268 - val_balanced_accuracy: 0.6008 - val_specificity: 0.9917 - val_miss_rate: 0.7900 - val_fall_out: 0.0083 - val_mcc: 0.3636
Epoch 25/100
7/7 [==============================] - 0s 60ms/step - loss: 1.4839 - accuracy: 0.4856 - recall: 0.2153 - precision: 0.7350 - AUROC: 0.8721 - AUPRC: 0.5244 - f1_score: 0.3330 - balanced_accuracy: 0.6033 - specificity: 0.9914 - miss_rate: 0.7847 - fall_out: 0.0086 - mcc: 0.3677 - val_loss: 1.4596 - val_accuracy: 0.5150 - val_recall: 0.2200 - val_precision: 0.7333 - val_AUROC: 0.8848 - val_AUPRC: 0.5160 - val_f1_score: 0.3385 - val_balanced_accuracy: 0.6056 - val_specificity: 0.9911 - val_miss_rate: 0.7800 - val_fall_out: 0.0089 - val_mcc: 0.3713
Epoch 26/100
7/7 [==============================] - 0s 59ms/step - loss: 1.4788 - accuracy: 0.4556 - recall: 0.2253 - precision: 0.7347 - AUROC: 0.8732 - AUPRC: 0.5122 - f1_score: 0.3448 - balanced_accuracy: 0.6081 - specificity: 0.9910 - miss_rate: 0.7747 - fall_out: 0.0090 - mcc: 0.3763 - val_loss: 1.4846 - val_accuracy: 0.5050 - val_recall: 0.2450 - val_precision: 0.7424 - val_AUROC: 0.8788 - val_AUPRC: 0.5210 - val_f1_score: 0.3684 - val_balanced_accuracy: 0.6178 - val_specificity: 0.9906 - val_miss_rate: 0.7550 - val_fall_out: 0.0094 - val_mcc: 0.3956
Epoch 27/100
7/7 [==============================] - 0s 59ms/step - loss: 1.5264 - accuracy: 0.4518 - recall: 0.2065 - precision: 0.7333 - AUROC: 0.8658 - AUPRC: 0.4923 - f1_score: 0.3223 - balanced_accuracy: 0.5991 - specificity: 0.9917 - miss_rate: 0.7935 - fall_out: 0.0083 - mcc: 0.3594 - val_loss: 1.4315 - val_accuracy: 0.5050 - val_recall: 0.2050 - val_precision: 0.7736 - val_AUROC: 0.8884 - val_AUPRC: 0.5339 - val_f1_score: 0.3241 - val_balanced_accuracy: 0.5992 - val_specificity: 0.9933 - val_miss_rate: 0.7950 - val_fall_out: 0.0067 - val_mcc: 0.3704
Epoch 28/100
7/7 [==============================] - 0s 60ms/step - loss: 1.4769 - accuracy: 0.4668 - recall: 0.2478 - precision: 0.7253 - AUROC: 0.8721 - AUPRC: 0.5195 - f1_score: 0.3694 - balanced_accuracy: 0.6187 - specificity: 0.9896 - miss_rate: 0.7522 - fall_out: 0.0104 - mcc: 0.3920 - val_loss: 1.4559 - val_accuracy: 0.4900 - val_recall: 0.2050 - val_precision: 0.7593 - val_AUROC: 0.8819 - val_AUPRC: 0.5257 - val_f1_score: 0.3228 - val_balanced_accuracy: 0.5989 - val_specificity: 0.9928 - val_miss_rate: 0.7950 - val_fall_out: 0.0072 - val_mcc: 0.3661
Epoch 29/100
7/7 [==============================] - 0s 59ms/step - loss: 1.4337 - accuracy: 0.4969 - recall: 0.2140 - precision: 0.7467 - AUROC: 0.8820 - AUPRC: 0.5381 - f1_score: 0.3327 - balanced_accuracy: 0.6030 - specificity: 0.9919 - miss_rate: 0.7860 - fall_out: 0.0081 - mcc: 0.3703 - val_loss: 1.4697 - val_accuracy: 0.4700 - val_recall: 0.2250 - val_precision: 0.7143 - val_AUROC: 0.8797 - val_AUPRC: 0.5158 - val_f1_score: 0.3422 - val_balanced_accuracy: 0.6075 - val_specificity: 0.9900 - val_miss_rate: 0.7750 - val_fall_out: 0.0100 - val_mcc: 0.3693
25/25 [==============================] - 0s 8ms/step - loss: 1.3024 - accuracy: 0.5695 - recall: 0.2516 - precision: 0.8627 - AUROC: 0.9051 - AUPRC: 0.6107 - f1_score: 0.3895 - balanced_accuracy: 0.6236 - specificity: 0.9955 - miss_rate: 0.7484 - fall_out: 0.0045 - mcc: 0.4406
7/7 [==============================] - 0s 8ms/step - loss: 1.4697 - accuracy: 0.4700 - recall: 0.2250 - precision: 0.7143 - AUROC: 0.8797 - AUPRC: 0.5158 - f1_score: 0.3422 - balanced_accuracy: 0.6075 - specificity: 0.9900 - miss_rate: 0.7750 - fall_out: 0.0100 - mcc: 0.3693
8it [01:37, 12.51s/it]
-- HOLDOUT 9
Model: "CNN_MelS_30s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_152 (Conv2D) (None, 100, 100, 128) 1280
max_pooling2d_152 (MaxPooli (None, 50, 50, 128) 0
ng2D)
conv2d_153 (Conv2D) (None, 50, 50, 64) 73792
max_pooling2d_153 (MaxPooli (None, 25, 25, 64) 0
ng2D)
conv2d_154 (Conv2D) (None, 25, 25, 64) 36928
max_pooling2d_154 (MaxPooli (None, 13, 13, 64) 0
ng2D)
conv2d_155 (Conv2D) (None, 13, 13, 128) 73856
max_pooling2d_155 (MaxPooli (None, 7, 7, 128) 0
ng2D)
flatten_38 (Flatten) (None, 6272) 0
dense_80 (Dense) (None, 128) 802944
dropout_40 (Dropout) (None, 128) 0
dense_81 (Dense) (None, 10) 1290
=================================================================
Total params: 990,090
Trainable params: 990,090
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
7/7 [==============================] - 2s 132ms/step - loss: 2.3114 - accuracy: 0.0914 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.4870 - AUPRC: 0.0950 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.3010 - val_accuracy: 0.1000 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5390 - val_AUPRC: 0.1304 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 2/100
7/7 [==============================] - 0s 60ms/step - loss: 2.3014 - accuracy: 0.1151 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.5125 - AUPRC: 0.1051 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.2982 - val_accuracy: 0.1000 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5311 - val_AUPRC: 0.1103 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 3/100
7/7 [==============================] - 0s 59ms/step - loss: 2.2905 - accuracy: 0.1264 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.5497 - AUPRC: 0.1194 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.2645 - val_accuracy: 0.1000 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5624 - val_AUPRC: 0.1430 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 4/100
7/7 [==============================] - 0s 59ms/step - loss: 2.2483 - accuracy: 0.1364 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.5880 - AUPRC: 0.1397 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.1911 - val_accuracy: 0.2100 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6717 - val_AUPRC: 0.2084 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 5/100
7/7 [==============================] - 0s 59ms/step - loss: 2.1953 - accuracy: 0.1940 - recall: 0.0063 - precision: 0.4545 - AUROC: 0.6309 - AUPRC: 0.1701 - f1_score: 0.0123 - balanced_accuracy: 0.5027 - specificity: 0.9992 - miss_rate: 0.9937 - fall_out: 8.3438e-04 - mcc: 0.0439 - val_loss: 2.1913 - val_accuracy: 0.1950 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6598 - val_AUPRC: 0.2104 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 6/100
7/7 [==============================] - 0s 59ms/step - loss: 2.1486 - accuracy: 0.2053 - recall: 0.0113 - precision: 0.6000 - AUROC: 0.6630 - AUPRC: 0.1934 - f1_score: 0.0221 - balanced_accuracy: 0.5052 - specificity: 0.9992 - miss_rate: 0.9887 - fall_out: 8.3438e-04 - mcc: 0.0723 - val_loss: 2.1572 - val_accuracy: 0.1900 - val_recall: 0.0100 - val_precision: 1.0000 - val_AUROC: 0.6699 - val_AUPRC: 0.2226 - val_f1_score: 0.0198 - val_balanced_accuracy: 0.5050 - val_specificity: 1.0000 - val_miss_rate: 0.9900 - val_fall_out: 0.0000e+00 - val_mcc: 0.0949
Epoch 7/100
7/7 [==============================] - 0s 59ms/step - loss: 2.1539 - accuracy: 0.2003 - recall: 0.0300 - precision: 0.5000 - AUROC: 0.6682 - AUPRC: 0.1966 - f1_score: 0.0567 - balanced_accuracy: 0.5134 - specificity: 0.9967 - miss_rate: 0.9700 - fall_out: 0.0033 - mcc: 0.1037 - val_loss: 2.1821 - val_accuracy: 0.2350 - val_recall: 0.0050 - val_precision: 1.0000 - val_AUROC: 0.6491 - val_AUPRC: 0.2253 - val_f1_score: 0.0100 - val_balanced_accuracy: 0.5025 - val_specificity: 1.0000 - val_miss_rate: 0.9950 - val_fall_out: 0.0000e+00 - val_mcc: 0.0671
Epoch 8/100
7/7 [==============================] - 0s 59ms/step - loss: 2.1276 - accuracy: 0.2065 - recall: 0.0225 - precision: 0.5625 - AUROC: 0.6715 - AUPRC: 0.2059 - f1_score: 0.0433 - balanced_accuracy: 0.5103 - specificity: 0.9981 - miss_rate: 0.9775 - fall_out: 0.0019 - mcc: 0.0978 - val_loss: 2.0591 - val_accuracy: 0.3000 - val_recall: 0.0150 - val_precision: 0.5000 - val_AUROC: 0.7295 - val_AUPRC: 0.2656 - val_f1_score: 0.0291 - val_balanced_accuracy: 0.5067 - val_specificity: 0.9983 - val_miss_rate: 0.9850 - val_fall_out: 0.0017 - val_mcc: 0.0731
Epoch 9/100
7/7 [==============================] - 0s 59ms/step - loss: 2.0765 - accuracy: 0.2303 - recall: 0.0325 - precision: 0.7647 - AUROC: 0.7054 - AUPRC: 0.2452 - f1_score: 0.0624 - balanced_accuracy: 0.5157 - specificity: 0.9989 - miss_rate: 0.9675 - fall_out: 0.0011 - mcc: 0.1448 - val_loss: 2.0239 - val_accuracy: 0.2950 - val_recall: 0.0550 - val_precision: 0.5500 - val_AUROC: 0.7471 - val_AUPRC: 0.2750 - val_f1_score: 0.1000 - val_balanced_accuracy: 0.5250 - val_specificity: 0.9950 - val_miss_rate: 0.9450 - val_fall_out: 0.0050 - val_mcc: 0.1508
Epoch 10/100
7/7 [==============================] - 0s 59ms/step - loss: 2.0176 - accuracy: 0.2641 - recall: 0.0576 - precision: 0.7188 - AUROC: 0.7177 - AUPRC: 0.2681 - f1_score: 0.1066 - balanced_accuracy: 0.5275 - specificity: 0.9975 - miss_rate: 0.9424 - fall_out: 0.0025 - mcc: 0.1853 - val_loss: 1.9356 - val_accuracy: 0.3350 - val_recall: 0.0150 - val_precision: 0.5000 - val_AUROC: 0.7629 - val_AUPRC: 0.3351 - val_f1_score: 0.0291 - val_balanced_accuracy: 0.5067 - val_specificity: 0.9983 - val_miss_rate: 0.9850 - val_fall_out: 0.0017 - val_mcc: 0.0731
Epoch 11/100
7/7 [==============================] - 0s 59ms/step - loss: 1.9159 - accuracy: 0.2991 - recall: 0.1001 - precision: 0.7143 - AUROC: 0.7514 - AUPRC: 0.3174 - f1_score: 0.1756 - balanced_accuracy: 0.5478 - specificity: 0.9955 - miss_rate: 0.8999 - fall_out: 0.0045 - mcc: 0.2441 - val_loss: 1.8400 - val_accuracy: 0.3550 - val_recall: 0.0650 - val_precision: 0.7222 - val_AUROC: 0.7987 - val_AUPRC: 0.3672 - val_f1_score: 0.1193 - val_balanced_accuracy: 0.5311 - val_specificity: 0.9972 - val_miss_rate: 0.9350 - val_fall_out: 0.0028 - val_mcc: 0.1977
Epoch 12/100
7/7 [==============================] - 0s 59ms/step - loss: 1.9083 - accuracy: 0.3129 - recall: 0.0989 - precision: 0.6639 - AUROC: 0.7571 - AUPRC: 0.3196 - f1_score: 0.1721 - balanced_accuracy: 0.5467 - specificity: 0.9944 - miss_rate: 0.9011 - fall_out: 0.0056 - mcc: 0.2311 - val_loss: 1.8952 - val_accuracy: 0.3000 - val_recall: 0.0900 - val_precision: 0.7826 - val_AUROC: 0.7708 - val_AUPRC: 0.3354 - val_f1_score: 0.1614 - val_balanced_accuracy: 0.5436 - val_specificity: 0.9972 - val_miss_rate: 0.9100 - val_fall_out: 0.0028 - val_mcc: 0.2454
Epoch 13/100
7/7 [==============================] - 0s 59ms/step - loss: 1.8904 - accuracy: 0.2966 - recall: 0.0814 - precision: 0.7386 - AUROC: 0.7674 - AUPRC: 0.3340 - f1_score: 0.1466 - balanced_accuracy: 0.5391 - specificity: 0.9968 - miss_rate: 0.9186 - fall_out: 0.0032 - mcc: 0.2246 - val_loss: 1.8730 - val_accuracy: 0.3400 - val_recall: 0.0900 - val_precision: 0.6429 - val_AUROC: 0.8019 - val_AUPRC: 0.3477 - val_f1_score: 0.1579 - val_balanced_accuracy: 0.5422 - val_specificity: 0.9944 - val_miss_rate: 0.9100 - val_fall_out: 0.0056 - val_mcc: 0.2156
25/25 [==============================] - 0s 8ms/step - loss: 1.7921 - accuracy: 0.3579 - recall: 0.1277 - precision: 0.7445 - AUROC: 0.8179 - AUPRC: 0.3952 - f1_score: 0.2179 - balanced_accuracy: 0.5614 - specificity: 0.9951 - miss_rate: 0.8723 - fall_out: 0.0049 - mcc: 0.2838
7/7 [==============================] - 0s 8ms/step - loss: 1.8730 - accuracy: 0.3400 - recall: 0.0900 - precision: 0.6429 - AUROC: 0.8019 - AUPRC: 0.3477 - f1_score: 0.1579 - balanced_accuracy: 0.5422 - specificity: 0.9944 - miss_rate: 0.9100 - fall_out: 0.0056 - mcc: 0.2156
9it [01:44, 10.87s/it]
-- HOLDOUT 10
Model: "CNN_MelS_30s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_156 (Conv2D) (None, 100, 100, 128) 1280
max_pooling2d_156 (MaxPooli (None, 50, 50, 128) 0
ng2D)
conv2d_157 (Conv2D) (None, 50, 50, 64) 73792
max_pooling2d_157 (MaxPooli (None, 25, 25, 64) 0
ng2D)
conv2d_158 (Conv2D) (None, 25, 25, 64) 36928
max_pooling2d_158 (MaxPooli (None, 13, 13, 64) 0
ng2D)
conv2d_159 (Conv2D) (None, 13, 13, 128) 73856
max_pooling2d_159 (MaxPooli (None, 7, 7, 128) 0
ng2D)
flatten_39 (Flatten) (None, 6272) 0
dense_82 (Dense) (None, 128) 802944
dropout_41 (Dropout) (None, 128) 0
dense_83 (Dense) (None, 10) 1290
=================================================================
Total params: 990,090
Trainable params: 990,090
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
7/7 [==============================] - 2s 132ms/step - loss: 2.3129 - accuracy: 0.0763 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.4946 - AUPRC: 0.0987 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.3016 - val_accuracy: 0.1000 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5325 - val_AUPRC: 0.1093 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 2/100
7/7 [==============================] - 0s 59ms/step - loss: 2.3027 - accuracy: 0.1039 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.5066 - AUPRC: 0.1035 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.2997 - val_accuracy: 0.1000 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5296 - val_AUPRC: 0.1370 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 3/100
7/7 [==============================] - 0s 59ms/step - loss: 2.2989 - accuracy: 0.1139 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.5147 - AUPRC: 0.1081 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.2885 - val_accuracy: 0.1150 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.5817 - val_AUPRC: 0.1583 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 4/100
7/7 [==============================] - 0s 59ms/step - loss: 2.2831 - accuracy: 0.1489 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.5711 - AUPRC: 0.1227 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.2440 - val_accuracy: 0.2000 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6296 - val_AUPRC: 0.1835 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 5/100
7/7 [==============================] - 0s 59ms/step - loss: 2.2209 - accuracy: 0.1777 - recall: 0.0000e+00 - precision: 0.0000e+00 - AUROC: 0.6251 - AUPRC: 0.1561 - f1_score: 0.0000e+00 - balanced_accuracy: 0.5000 - specificity: 1.0000 - miss_rate: 1.0000 - fall_out: 0.0000e+00 - mcc: 0.0000e+00 - val_loss: 2.1338 - val_accuracy: 0.2500 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7224 - val_AUPRC: 0.2643 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 6/100
7/7 [==============================] - 0s 59ms/step - loss: 2.1372 - accuracy: 0.2178 - recall: 0.0150 - precision: 0.5455 - AUROC: 0.6615 - AUPRC: 0.2002 - f1_score: 0.0292 - balanced_accuracy: 0.5068 - specificity: 0.9986 - miss_rate: 0.9850 - fall_out: 0.0014 - mcc: 0.0780 - val_loss: 2.1110 - val_accuracy: 0.1950 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.6877 - val_AUPRC: 0.2380 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 7/100
7/7 [==============================] - 0s 59ms/step - loss: 2.1054 - accuracy: 0.2103 - recall: 0.0063 - precision: 0.4545 - AUROC: 0.6893 - AUPRC: 0.2071 - f1_score: 0.0123 - balanced_accuracy: 0.5027 - specificity: 0.9992 - miss_rate: 0.9937 - fall_out: 8.3438e-04 - mcc: 0.0439 - val_loss: 2.0048 - val_accuracy: 0.2700 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7539 - val_AUPRC: 0.2937 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 8/100
7/7 [==============================] - 0s 59ms/step - loss: 1.9883 - accuracy: 0.2278 - recall: 0.0275 - precision: 0.5500 - AUROC: 0.7359 - AUPRC: 0.2576 - f1_score: 0.0524 - balanced_accuracy: 0.5125 - specificity: 0.9975 - miss_rate: 0.9725 - fall_out: 0.0025 - mcc: 0.1064 - val_loss: 1.9955 - val_accuracy: 0.2450 - val_recall: 0.0700 - val_precision: 0.6087 - val_AUROC: 0.7324 - val_AUPRC: 0.2726 - val_f1_score: 0.1256 - val_balanced_accuracy: 0.5325 - val_specificity: 0.9950 - val_miss_rate: 0.9300 - val_fall_out: 0.0050 - val_mcc: 0.1829
Epoch 9/100
7/7 [==============================] - 0s 60ms/step - loss: 2.0134 - accuracy: 0.2516 - recall: 0.0663 - precision: 0.6092 - AUROC: 0.7250 - AUPRC: 0.2503 - f1_score: 0.1196 - balanced_accuracy: 0.5308 - specificity: 0.9953 - miss_rate: 0.9337 - fall_out: 0.0047 - mcc: 0.1781 - val_loss: 1.9156 - val_accuracy: 0.2800 - val_recall: 0.0700 - val_precision: 0.8750 - val_AUROC: 0.7866 - val_AUPRC: 0.3358 - val_f1_score: 0.1296 - val_balanced_accuracy: 0.5344 - val_specificity: 0.9989 - val_miss_rate: 0.9300 - val_fall_out: 0.0011 - val_mcc: 0.2320
Epoch 10/100
7/7 [==============================] - 0s 59ms/step - loss: 1.9604 - accuracy: 0.2566 - recall: 0.0375 - precision: 0.6250 - AUROC: 0.7498 - AUPRC: 0.2754 - f1_score: 0.0708 - balanced_accuracy: 0.5175 - specificity: 0.9975 - miss_rate: 0.9625 - fall_out: 0.0025 - mcc: 0.1360 - val_loss: 1.9187 - val_accuracy: 0.3300 - val_recall: 0.0200 - val_precision: 1.0000 - val_AUROC: 0.7814 - val_AUPRC: 0.3388 - val_f1_score: 0.0392 - val_balanced_accuracy: 0.5100 - val_specificity: 1.0000 - val_miss_rate: 0.9800 - val_fall_out: 0.0000e+00 - val_mcc: 0.1343
Epoch 11/100
7/7 [==============================] - 0s 59ms/step - loss: 1.8708 - accuracy: 0.3041 - recall: 0.0926 - precision: 0.6667 - AUROC: 0.7774 - AUPRC: 0.3163 - f1_score: 0.1626 - balanced_accuracy: 0.5437 - specificity: 0.9949 - miss_rate: 0.9074 - fall_out: 0.0051 - mcc: 0.2242 - val_loss: 1.8064 - val_accuracy: 0.3250 - val_recall: 0.0850 - val_precision: 0.7391 - val_AUROC: 0.8093 - val_AUPRC: 0.3543 - val_f1_score: 0.1525 - val_balanced_accuracy: 0.5408 - val_specificity: 0.9967 - val_miss_rate: 0.9150 - val_fall_out: 0.0033 - val_mcc: 0.2298
Epoch 12/100
7/7 [==============================] - 0s 59ms/step - loss: 1.8749 - accuracy: 0.2829 - recall: 0.0976 - precision: 0.6094 - AUROC: 0.7712 - AUPRC: 0.3157 - f1_score: 0.1683 - balanced_accuracy: 0.5453 - specificity: 0.9930 - miss_rate: 0.9024 - fall_out: 0.0070 - mcc: 0.2166 - val_loss: 1.8475 - val_accuracy: 0.3550 - val_recall: 0.1550 - val_precision: 0.6458 - val_AUROC: 0.7884 - val_AUPRC: 0.3532 - val_f1_score: 0.2500 - val_balanced_accuracy: 0.5728 - val_specificity: 0.9906 - val_miss_rate: 0.8450 - val_fall_out: 0.0094 - val_mcc: 0.2853
Epoch 13/100
7/7 [==============================] - 0s 59ms/step - loss: 1.8249 - accuracy: 0.3254 - recall: 0.1026 - precision: 0.6508 - AUROC: 0.7872 - AUPRC: 0.3517 - f1_score: 0.1773 - balanced_accuracy: 0.5483 - specificity: 0.9939 - miss_rate: 0.8974 - fall_out: 0.0061 - mcc: 0.2324 - val_loss: 1.8620 - val_accuracy: 0.2850 - val_recall: 0.1300 - val_precision: 0.5909 - val_AUROC: 0.7823 - val_AUPRC: 0.3284 - val_f1_score: 0.2131 - val_balanced_accuracy: 0.5600 - val_specificity: 0.9900 - val_miss_rate: 0.8700 - val_fall_out: 0.0100 - val_mcc: 0.2454
25/25 [==============================] - 0s 8ms/step - loss: 1.8127 - accuracy: 0.3091 - recall: 0.1364 - precision: 0.6728 - AUROC: 0.7944 - AUPRC: 0.3546 - f1_score: 0.2268 - balanced_accuracy: 0.5645 - specificity: 0.9926 - miss_rate: 0.8636 - fall_out: 0.0074 - mcc: 0.2747
7/7 [==============================] - 0s 8ms/step - loss: 1.8620 - accuracy: 0.2850 - recall: 0.1300 - precision: 0.5909 - AUROC: 0.7823 - AUPRC: 0.3284 - f1_score: 0.2131 - balanced_accuracy: 0.5600 - specificity: 0.9900 - miss_rate: 0.8700 - fall_out: 0.0100 - mcc: 0.2454
10it [01:51, 11.17s/it]
CNN_metrics_estimate = model_metrics_holdout_estimate(CNN_MelS_30s_metrics, number_of_splits)
print(f"CNN Mel spectrogram 30s Metrics - {number_of_splits}-holdouts estimate:")
print(f"Accuracy : train - {CNN_metrics_estimate['accuracy_train']} -- test - {CNN_metrics_estimate['accuracy_test']}")
print(f"AUROC : train - {CNN_metrics_estimate['AUROC_train']} -- test - {CNN_metrics_estimate['AUROC_test']}")
print(f"AUPRC : train - {CNN_metrics_estimate['AUPRC_train']} -- test - {CNN_metrics_estimate['AUPRC_test']}")
print("-"*80)
print("CNN - Train history:")
plot_train_history(CNN_MelS_30s_history)
print("-"*100)
CNN Mel spectrogram 30s Metrics - 10-holdouts estimate: Accuracy : train - 0.47622027397155764 -- test - 0.4054999977350235 AUROC : train - 0.8707141101360321 -- test - 0.8398672342300415 AUPRC : train - 0.521598556637764 -- test - 0.4434808850288391 -------------------------------------------------------------------------------- CNN - Train history:
----------------------------------------------------------------------------------------------------
data['spectrogram'][sample].shape
(1025, 130)
print("---- 3s window Spectrogram - Fixed CNN ----")
resized_input_data = resize(data['spectrogram'], (130, 100))
input_data = [np.expand_dims(x, axis=-1) for x in resized_input_data]
data_labels = data['labels_3s']
CNN_S_3s_metrics = []
CNN_S_3s_history = []
# Generate holdouts
for holdout_number, (train_indices, test_indices) in enumerate(tqdm(holdouts_generator.split(input_data, data_labels))):
print(f"-- HOLDOUT {holdout_number+1}")
# Train/Test data
x_train, x_test = np.array([input_data[x] for x in train_indices]), np.array([input_data[x] for x in test_indices])
y_train, y_test = data_labels.iloc[train_indices], data_labels.iloc[test_indices]
# One-hot encoding
y_train = one_hot_encoding(y_train, 10)
y_test = one_hot_encoding(y_test, 10)
# Build CNN
CNN = build_fixed_CNN_S(x_train.shape[1:])
print("- Training model:\n")
CNN_holdout_metrics, CNN_holdout_history = train_model(
CNN,
np.array(x_train),
np.array(x_test),
y_train.values,
y_test.values,
epochs,
batch_size
)
CNN_S_3s_metrics.append(CNN_holdout_metrics)
CNN_S_3s_history.append(CNN_holdout_history)
---- 3s window Spectrogram - Fixed CNN ----
0it [00:00, ?it/s]
-- HOLDOUT 1
Model: "CNN_S_3s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_88 (Conv2D) (None, 100, 130, 128) 1280
max_pooling2d_67 (MaxPoolin (None, 50, 65, 128) 0
g2D)
conv2d_89 (Conv2D) (None, 50, 65, 64) 73792
max_pooling2d_68 (MaxPoolin (None, 25, 33, 64) 0
g2D)
conv2d_90 (Conv2D) (None, 25, 33, 64) 36928
max_pooling2d_69 (MaxPoolin (None, 13, 17, 64) 0
g2D)
conv2d_91 (Conv2D) (None, 13, 17, 128) 131200
flatten_22 (Flatten) (None, 28288) 0
dense_66 (Dense) (None, 128) 3620992
dropout_46 (Dropout) (None, 128) 0
dense_67 (Dense) (None, 128) 16512
dropout_47 (Dropout) (None, 128) 0
dense_68 (Dense) (None, 10) 1290
=================================================================
Total params: 3,881,994
Trainable params: 3,881,994
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 6s 82ms/step - loss: 2.1703 - accuracy: 0.2014 - recall: 0.0292 - precision: 0.5884 - AUROC: 0.6438 - AUPRC: 0.1998 - f1_score: 0.0556 - balanced_accuracy: 0.5135 - specificity: 0.9977 - miss_rate: 0.9708 - fall_out: 0.0023 - mcc: 0.1149 - val_loss: 1.8254 - val_accuracy: 0.3110 - val_recall: 0.1267 - val_precision: 0.7857 - val_AUROC: 0.8046 - val_AUPRC: 0.3882 - val_f1_score: 0.2182 - val_balanced_accuracy: 0.5614 - val_specificity: 0.9962 - val_miss_rate: 0.8733 - val_fall_out: 0.0038 - val_mcc: 0.2926
Epoch 2/100
63/63 [==============================] - 5s 73ms/step - loss: 1.8346 - accuracy: 0.3388 - recall: 0.1346 - precision: 0.6774 - AUROC: 0.7883 - AUPRC: 0.3649 - f1_score: 0.2246 - balanced_accuracy: 0.5638 - specificity: 0.9929 - miss_rate: 0.8654 - fall_out: 0.0071 - mcc: 0.2741 - val_loss: 1.5598 - val_accuracy: 0.4316 - val_recall: 0.2178 - val_precision: 0.7461 - val_AUROC: 0.8655 - val_AUPRC: 0.4952 - val_f1_score: 0.3372 - val_balanced_accuracy: 0.6048 - val_specificity: 0.9918 - val_miss_rate: 0.7822 - val_fall_out: 0.0082 - val_mcc: 0.3735
Epoch 3/100
63/63 [==============================] - 5s 73ms/step - loss: 1.6783 - accuracy: 0.3859 - recall: 0.1752 - precision: 0.6875 - AUROC: 0.8323 - AUPRC: 0.4253 - f1_score: 0.2793 - balanced_accuracy: 0.5832 - specificity: 0.9911 - miss_rate: 0.8248 - fall_out: 0.0089 - mcc: 0.3167 - val_loss: 1.4667 - val_accuracy: 0.4917 - val_recall: 0.2364 - val_precision: 0.8027 - val_AUROC: 0.8852 - val_AUPRC: 0.5412 - val_f1_score: 0.3652 - val_balanced_accuracy: 0.6150 - val_specificity: 0.9935 - val_miss_rate: 0.7636 - val_fall_out: 0.0065 - val_mcc: 0.4080
Epoch 4/100
63/63 [==============================] - 5s 73ms/step - loss: 1.5518 - accuracy: 0.4355 - recall: 0.2248 - precision: 0.7223 - AUROC: 0.8606 - AUPRC: 0.4863 - f1_score: 0.3429 - balanced_accuracy: 0.6076 - specificity: 0.9904 - miss_rate: 0.7752 - fall_out: 0.0096 - mcc: 0.3718 - val_loss: 1.4370 - val_accuracy: 0.5013 - val_recall: 0.2083 - val_precision: 0.8046 - val_AUROC: 0.8888 - val_AUPRC: 0.5442 - val_f1_score: 0.3309 - val_balanced_accuracy: 0.6013 - val_specificity: 0.9944 - val_miss_rate: 0.7917 - val_fall_out: 0.0056 - val_mcc: 0.3829
Epoch 5/100
63/63 [==============================] - 5s 73ms/step - loss: 1.4217 - accuracy: 0.4821 - recall: 0.2807 - precision: 0.7482 - AUROC: 0.8846 - AUPRC: 0.5479 - f1_score: 0.4082 - balanced_accuracy: 0.6351 - specificity: 0.9895 - miss_rate: 0.7193 - fall_out: 0.0105 - mcc: 0.4266 - val_loss: 1.2303 - val_accuracy: 0.5699 - val_recall: 0.3205 - val_precision: 0.8247 - val_AUROC: 0.9181 - val_AUPRC: 0.6401 - val_f1_score: 0.4616 - val_balanced_accuracy: 0.6565 - val_specificity: 0.9924 - val_miss_rate: 0.6795 - val_fall_out: 0.0076 - val_mcc: 0.4857
Epoch 6/100
63/63 [==============================] - 5s 73ms/step - loss: 1.3371 - accuracy: 0.5263 - recall: 0.3163 - precision: 0.7564 - AUROC: 0.8983 - AUPRC: 0.5887 - f1_score: 0.4460 - balanced_accuracy: 0.6525 - specificity: 0.9887 - miss_rate: 0.6837 - fall_out: 0.0113 - mcc: 0.4571 - val_loss: 1.2125 - val_accuracy: 0.5578 - val_recall: 0.3575 - val_precision: 0.7838 - val_AUROC: 0.9167 - val_AUPRC: 0.6395 - val_f1_score: 0.4911 - val_balanced_accuracy: 0.6733 - val_specificity: 0.9890 - val_miss_rate: 0.6425 - val_fall_out: 0.0110 - val_mcc: 0.4983
Epoch 7/100
63/63 [==============================] - 5s 73ms/step - loss: 1.2412 - accuracy: 0.5576 - recall: 0.3647 - precision: 0.7741 - AUROC: 0.9126 - AUPRC: 0.6318 - f1_score: 0.4958 - balanced_accuracy: 0.6765 - specificity: 0.9882 - miss_rate: 0.6353 - fall_out: 0.0118 - mcc: 0.4996 - val_loss: 1.0998 - val_accuracy: 0.6214 - val_recall: 0.4071 - val_precision: 0.8279 - val_AUROC: 0.9338 - val_AUPRC: 0.7033 - val_f1_score: 0.5458 - val_balanced_accuracy: 0.6989 - val_specificity: 0.9906 - val_miss_rate: 0.5929 - val_fall_out: 0.0094 - val_mcc: 0.5518
Epoch 8/100
63/63 [==============================] - 5s 73ms/step - loss: 1.1524 - accuracy: 0.5899 - recall: 0.4113 - precision: 0.7953 - AUROC: 0.9245 - AUPRC: 0.6737 - f1_score: 0.5422 - balanced_accuracy: 0.6998 - specificity: 0.9882 - miss_rate: 0.5887 - fall_out: 0.0118 - mcc: 0.5413 - val_loss: 1.0715 - val_accuracy: 0.6385 - val_recall: 0.4166 - val_precision: 0.8498 - val_AUROC: 0.9363 - val_AUPRC: 0.7145 - val_f1_score: 0.5591 - val_balanced_accuracy: 0.7042 - val_specificity: 0.9918 - val_miss_rate: 0.5834 - val_fall_out: 0.0082 - val_mcc: 0.5675
Epoch 9/100
63/63 [==============================] - 5s 73ms/step - loss: 1.0412 - accuracy: 0.6369 - recall: 0.4677 - precision: 0.8128 - AUROC: 0.9392 - AUPRC: 0.7216 - f1_score: 0.5937 - balanced_accuracy: 0.7279 - specificity: 0.9880 - miss_rate: 0.5323 - fall_out: 0.0120 - mcc: 0.5871 - val_loss: 1.0185 - val_accuracy: 0.6610 - val_recall: 0.4647 - val_precision: 0.8391 - val_AUROC: 0.9423 - val_AUPRC: 0.7383 - val_f1_score: 0.5981 - val_balanced_accuracy: 0.7274 - val_specificity: 0.9901 - val_miss_rate: 0.5353 - val_fall_out: 0.0099 - val_mcc: 0.5965
Epoch 10/100
63/63 [==============================] - 5s 73ms/step - loss: 0.9781 - accuracy: 0.6607 - recall: 0.5132 - precision: 0.8181 - AUROC: 0.9458 - AUPRC: 0.7492 - f1_score: 0.6307 - balanced_accuracy: 0.7502 - specificity: 0.9873 - miss_rate: 0.4868 - fall_out: 0.0127 - mcc: 0.6192 - val_loss: 1.0424 - val_accuracy: 0.6675 - val_recall: 0.5273 - val_precision: 0.8020 - val_AUROC: 0.9388 - val_AUPRC: 0.7383 - val_f1_score: 0.6363 - val_balanced_accuracy: 0.7564 - val_specificity: 0.9855 - val_miss_rate: 0.4727 - val_fall_out: 0.0145 - val_mcc: 0.6207
Epoch 11/100
63/63 [==============================] - 5s 73ms/step - loss: 0.9024 - accuracy: 0.6831 - recall: 0.5493 - precision: 0.8312 - AUROC: 0.9538 - AUPRC: 0.7808 - f1_score: 0.6615 - balanced_accuracy: 0.7685 - specificity: 0.9876 - miss_rate: 0.4507 - fall_out: 0.0124 - mcc: 0.6484 - val_loss: 0.9826 - val_accuracy: 0.6750 - val_recall: 0.5578 - val_precision: 0.8067 - val_AUROC: 0.9455 - val_AUPRC: 0.7593 - val_f1_score: 0.6596 - val_balanced_accuracy: 0.7715 - val_specificity: 0.9851 - val_miss_rate: 0.4422 - val_fall_out: 0.0149 - val_mcc: 0.6420
Epoch 12/100
63/63 [==============================] - 5s 73ms/step - loss: 0.8571 - accuracy: 0.6950 - recall: 0.5735 - precision: 0.8298 - AUROC: 0.9578 - AUPRC: 0.7947 - f1_score: 0.6783 - balanced_accuracy: 0.7802 - specificity: 0.9869 - miss_rate: 0.4265 - fall_out: 0.0131 - mcc: 0.6629 - val_loss: 0.9129 - val_accuracy: 0.6980 - val_recall: 0.5734 - val_precision: 0.8103 - val_AUROC: 0.9522 - val_AUPRC: 0.7792 - val_f1_score: 0.6716 - val_balanced_accuracy: 0.7792 - val_specificity: 0.9851 - val_miss_rate: 0.4266 - val_fall_out: 0.0149 - val_mcc: 0.6534
Epoch 13/100
63/63 [==============================] - 5s 73ms/step - loss: 0.7823 - accuracy: 0.7261 - recall: 0.6169 - precision: 0.8472 - AUROC: 0.9651 - AUPRC: 0.8248 - f1_score: 0.7139 - balanced_accuracy: 0.8023 - specificity: 0.9876 - miss_rate: 0.3831 - fall_out: 0.0124 - mcc: 0.6980 - val_loss: 0.9572 - val_accuracy: 0.6860 - val_recall: 0.5774 - val_precision: 0.8040 - val_AUROC: 0.9494 - val_AUPRC: 0.7725 - val_f1_score: 0.6721 - val_balanced_accuracy: 0.7809 - val_specificity: 0.9844 - val_miss_rate: 0.4226 - val_fall_out: 0.0156 - val_mcc: 0.6527
Epoch 14/100
63/63 [==============================] - 5s 73ms/step - loss: 0.7379 - accuracy: 0.7472 - recall: 0.6397 - precision: 0.8609 - AUROC: 0.9686 - AUPRC: 0.8416 - f1_score: 0.7340 - balanced_accuracy: 0.8141 - specificity: 0.9885 - miss_rate: 0.3603 - fall_out: 0.0115 - mcc: 0.7186 - val_loss: 0.9618 - val_accuracy: 0.7051 - val_recall: 0.6284 - val_precision: 0.7898 - val_AUROC: 0.9490 - val_AUPRC: 0.7800 - val_f1_score: 0.6999 - val_balanced_accuracy: 0.8049 - val_specificity: 0.9814 - val_miss_rate: 0.3716 - val_fall_out: 0.0186 - val_mcc: 0.6761
250/250 [==============================] - 2s 9ms/step - loss: 0.3866 - accuracy: 0.8826 - recall: 0.8074 - precision: 0.9446 - AUROC: 0.9926 - AUPRC: 0.9544 - f1_score: 0.8706 - balanced_accuracy: 0.9011 - specificity: 0.9947 - miss_rate: 0.1926 - fall_out: 0.0053 - mcc: 0.8607
63/63 [==============================] - 1s 10ms/step - loss: 0.9618 - accuracy: 0.7051 - recall: 0.6284 - precision: 0.7898 - AUROC: 0.9490 - AUPRC: 0.7800 - f1_score: 0.6999 - balanced_accuracy: 0.8049 - specificity: 0.9814 - miss_rate: 0.3716 - fall_out: 0.0186 - mcc: 0.6761
1it [01:09, 69.19s/it]
-- HOLDOUT 2
Model: "CNN_S_3s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_92 (Conv2D) (None, 100, 130, 128) 1280
max_pooling2d_70 (MaxPoolin (None, 50, 65, 128) 0
g2D)
conv2d_93 (Conv2D) (None, 50, 65, 64) 73792
max_pooling2d_71 (MaxPoolin (None, 25, 33, 64) 0
g2D)
conv2d_94 (Conv2D) (None, 25, 33, 64) 36928
max_pooling2d_72 (MaxPoolin (None, 13, 17, 64) 0
g2D)
conv2d_95 (Conv2D) (None, 13, 17, 128) 131200
flatten_23 (Flatten) (None, 28288) 0
dense_69 (Dense) (None, 128) 3620992
dropout_48 (Dropout) (None, 128) 0
dense_70 (Dense) (None, 128) 16512
dropout_49 (Dropout) (None, 128) 0
dense_71 (Dense) (None, 10) 1290
=================================================================
Total params: 3,881,994
Trainable params: 3,881,994
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 6s 80ms/step - loss: 2.1811 - accuracy: 0.2003 - recall: 0.0289 - precision: 0.5804 - AUROC: 0.6440 - AUPRC: 0.1933 - f1_score: 0.0551 - balanced_accuracy: 0.5133 - specificity: 0.9977 - miss_rate: 0.9711 - fall_out: 0.0023 - mcc: 0.1133 - val_loss: 1.8588 - val_accuracy: 0.3570 - val_recall: 0.0836 - val_precision: 0.8608 - val_AUROC: 0.8208 - val_AUPRC: 0.3853 - val_f1_score: 0.1524 - val_balanced_accuracy: 0.5411 - val_specificity: 0.9985 - val_miss_rate: 0.9164 - val_fall_out: 0.0015 - val_mcc: 0.2512
Epoch 2/100
63/63 [==============================] - 5s 73ms/step - loss: 1.8213 - accuracy: 0.3280 - recall: 0.1224 - precision: 0.6324 - AUROC: 0.7929 - AUPRC: 0.3489 - f1_score: 0.2051 - balanced_accuracy: 0.5572 - specificity: 0.9921 - miss_rate: 0.8776 - fall_out: 0.0079 - mcc: 0.2493 - val_loss: 1.5792 - val_accuracy: 0.4377 - val_recall: 0.1733 - val_precision: 0.8103 - val_AUROC: 0.8654 - val_AUPRC: 0.4925 - val_f1_score: 0.2855 - val_balanced_accuracy: 0.5844 - val_specificity: 0.9955 - val_miss_rate: 0.8267 - val_fall_out: 0.0045 - val_mcc: 0.3500
Epoch 3/100
63/63 [==============================] - 5s 73ms/step - loss: 1.6300 - accuracy: 0.4007 - recall: 0.1845 - precision: 0.6757 - AUROC: 0.8438 - AUPRC: 0.4363 - f1_score: 0.2898 - balanced_accuracy: 0.5873 - specificity: 0.9902 - miss_rate: 0.8155 - fall_out: 0.0098 - mcc: 0.3215 - val_loss: 1.4672 - val_accuracy: 0.4822 - val_recall: 0.2328 - val_precision: 0.7855 - val_AUROC: 0.8768 - val_AUPRC: 0.5277 - val_f1_score: 0.3592 - val_balanced_accuracy: 0.6129 - val_specificity: 0.9929 - val_miss_rate: 0.7672 - val_fall_out: 0.0071 - val_mcc: 0.3994
Epoch 4/100
63/63 [==============================] - 5s 73ms/step - loss: 1.5025 - accuracy: 0.4564 - recall: 0.2362 - precision: 0.7106 - AUROC: 0.8694 - AUPRC: 0.4987 - f1_score: 0.3546 - balanced_accuracy: 0.6128 - specificity: 0.9893 - miss_rate: 0.7638 - fall_out: 0.0107 - mcc: 0.3774 - val_loss: 1.2858 - val_accuracy: 0.5578 - val_recall: 0.2854 - val_precision: 0.7961 - val_AUROC: 0.9132 - val_AUPRC: 0.6236 - val_f1_score: 0.4202 - val_balanced_accuracy: 0.6387 - val_specificity: 0.9919 - val_miss_rate: 0.7146 - val_fall_out: 0.0081 - val_mcc: 0.4474
Epoch 5/100
63/63 [==============================] - 5s 73ms/step - loss: 1.3811 - accuracy: 0.5073 - recall: 0.2940 - precision: 0.7357 - AUROC: 0.8916 - AUPRC: 0.5604 - f1_score: 0.4201 - balanced_accuracy: 0.6411 - specificity: 0.9883 - miss_rate: 0.7060 - fall_out: 0.0117 - mcc: 0.4323 - val_loss: 1.1606 - val_accuracy: 0.5929 - val_recall: 0.3600 - val_precision: 0.8208 - val_AUROC: 0.9261 - val_AUPRC: 0.6738 - val_f1_score: 0.5005 - val_balanced_accuracy: 0.6757 - val_specificity: 0.9913 - val_miss_rate: 0.6400 - val_fall_out: 0.0087 - val_mcc: 0.5146
Epoch 6/100
63/63 [==============================] - 5s 73ms/step - loss: 1.2968 - accuracy: 0.5497 - recall: 0.3464 - precision: 0.7506 - AUROC: 0.9053 - AUPRC: 0.6029 - f1_score: 0.4741 - balanced_accuracy: 0.6668 - specificity: 0.9872 - miss_rate: 0.6536 - fall_out: 0.0128 - mcc: 0.4771 - val_loss: 1.1596 - val_accuracy: 0.6014 - val_recall: 0.3605 - val_precision: 0.8191 - val_AUROC: 0.9291 - val_AUPRC: 0.6831 - val_f1_score: 0.5007 - val_balanced_accuracy: 0.6758 - val_specificity: 0.9912 - val_miss_rate: 0.6395 - val_fall_out: 0.0088 - val_mcc: 0.5143
Epoch 7/100
63/63 [==============================] - 5s 73ms/step - loss: 1.1778 - accuracy: 0.5869 - recall: 0.4036 - precision: 0.7753 - AUROC: 0.9217 - AUPRC: 0.6553 - f1_score: 0.5308 - balanced_accuracy: 0.6953 - specificity: 0.9870 - miss_rate: 0.5964 - fall_out: 0.0130 - mcc: 0.5275 - val_loss: 1.1104 - val_accuracy: 0.6304 - val_recall: 0.4026 - val_precision: 0.8490 - val_AUROC: 0.9315 - val_AUPRC: 0.7089 - val_f1_score: 0.5462 - val_balanced_accuracy: 0.6973 - val_specificity: 0.9920 - val_miss_rate: 0.5974 - val_fall_out: 0.0080 - val_mcc: 0.5571
Epoch 8/100
63/63 [==============================] - 5s 73ms/step - loss: 1.1109 - accuracy: 0.6151 - recall: 0.4394 - precision: 0.7864 - AUROC: 0.9300 - AUPRC: 0.6867 - f1_score: 0.5638 - balanced_accuracy: 0.7131 - specificity: 0.9867 - miss_rate: 0.5606 - fall_out: 0.0133 - mcc: 0.5566 - val_loss: 1.0198 - val_accuracy: 0.6655 - val_recall: 0.4617 - val_precision: 0.8633 - val_AUROC: 0.9415 - val_AUPRC: 0.7465 - val_f1_score: 0.6016 - val_balanced_accuracy: 0.7268 - val_specificity: 0.9919 - val_miss_rate: 0.5383 - val_fall_out: 0.0081 - val_mcc: 0.6048
Epoch 9/100
63/63 [==============================] - 5s 74ms/step - loss: 1.0385 - accuracy: 0.6447 - recall: 0.4776 - precision: 0.7926 - AUROC: 0.9389 - AUPRC: 0.7195 - f1_score: 0.5960 - balanced_accuracy: 0.7318 - specificity: 0.9861 - miss_rate: 0.5224 - fall_out: 0.0139 - mcc: 0.5846 - val_loss: 1.0059 - val_accuracy: 0.6625 - val_recall: 0.5128 - val_precision: 0.8346 - val_AUROC: 0.9416 - val_AUPRC: 0.7485 - val_f1_score: 0.6352 - val_balanced_accuracy: 0.7507 - val_specificity: 0.9887 - val_miss_rate: 0.4872 - val_fall_out: 0.0113 - val_mcc: 0.6265
Epoch 10/100
63/63 [==============================] - 5s 73ms/step - loss: 0.9587 - accuracy: 0.6673 - recall: 0.5245 - precision: 0.8119 - AUROC: 0.9478 - AUPRC: 0.7512 - f1_score: 0.6373 - balanced_accuracy: 0.7555 - specificity: 0.9865 - miss_rate: 0.4755 - fall_out: 0.0135 - mcc: 0.6237 - val_loss: 0.9566 - val_accuracy: 0.6775 - val_recall: 0.5288 - val_precision: 0.8263 - val_AUROC: 0.9477 - val_AUPRC: 0.7668 - val_f1_score: 0.6449 - val_balanced_accuracy: 0.7582 - val_specificity: 0.9876 - val_miss_rate: 0.4712 - val_fall_out: 0.0124 - val_mcc: 0.6330
Epoch 11/100
63/63 [==============================] - 5s 73ms/step - loss: 0.8758 - accuracy: 0.6984 - recall: 0.5664 - precision: 0.8297 - AUROC: 0.9560 - AUPRC: 0.7875 - f1_score: 0.6732 - balanced_accuracy: 0.7767 - specificity: 0.9871 - miss_rate: 0.4336 - fall_out: 0.0129 - mcc: 0.6584 - val_loss: 0.9197 - val_accuracy: 0.7006 - val_recall: 0.5799 - val_precision: 0.8349 - val_AUROC: 0.9512 - val_AUPRC: 0.7867 - val_f1_score: 0.6844 - val_balanced_accuracy: 0.7836 - val_specificity: 0.9873 - val_miss_rate: 0.4201 - val_fall_out: 0.0127 - val_mcc: 0.6692
Epoch 12/100
63/63 [==============================] - 5s 74ms/step - loss: 0.8422 - accuracy: 0.7085 - recall: 0.5835 - precision: 0.8389 - AUROC: 0.9592 - AUPRC: 0.8013 - f1_score: 0.6883 - balanced_accuracy: 0.7855 - specificity: 0.9875 - miss_rate: 0.4165 - fall_out: 0.0125 - mcc: 0.6734 - val_loss: 0.9472 - val_accuracy: 0.6865 - val_recall: 0.5769 - val_precision: 0.8124 - val_AUROC: 0.9467 - val_AUPRC: 0.7758 - val_f1_score: 0.6747 - val_balanced_accuracy: 0.7810 - val_specificity: 0.9852 - val_miss_rate: 0.4231 - val_fall_out: 0.0148 - val_mcc: 0.6565
Epoch 13/100
63/63 [==============================] - 5s 73ms/step - loss: 0.7635 - accuracy: 0.7370 - recall: 0.6232 - precision: 0.8444 - AUROC: 0.9664 - AUPRC: 0.8287 - f1_score: 0.7172 - balanced_accuracy: 0.8052 - specificity: 0.9872 - miss_rate: 0.3768 - fall_out: 0.0128 - mcc: 0.7005 - val_loss: 0.8782 - val_accuracy: 0.7151 - val_recall: 0.6149 - val_precision: 0.8176 - val_AUROC: 0.9545 - val_AUPRC: 0.7984 - val_f1_score: 0.7019 - val_balanced_accuracy: 0.7998 - val_specificity: 0.9848 - val_miss_rate: 0.3851 - val_fall_out: 0.0152 - val_mcc: 0.6821
Epoch 14/100
63/63 [==============================] - 5s 73ms/step - loss: 0.7312 - accuracy: 0.7482 - recall: 0.6418 - precision: 0.8527 - AUROC: 0.9694 - AUPRC: 0.8423 - f1_score: 0.7324 - balanced_accuracy: 0.8147 - specificity: 0.9877 - miss_rate: 0.3582 - fall_out: 0.0123 - mcc: 0.7158 - val_loss: 0.8850 - val_accuracy: 0.7236 - val_recall: 0.6565 - val_precision: 0.8184 - val_AUROC: 0.9534 - val_AUPRC: 0.8112 - val_f1_score: 0.7285 - val_balanced_accuracy: 0.8201 - val_specificity: 0.9838 - val_miss_rate: 0.3435 - val_fall_out: 0.0162 - val_mcc: 0.7072
Epoch 15/100
63/63 [==============================] - 5s 75ms/step - loss: 0.6594 - accuracy: 0.7752 - recall: 0.6894 - precision: 0.8580 - AUROC: 0.9743 - AUPRC: 0.8652 - f1_score: 0.7645 - balanced_accuracy: 0.8384 - specificity: 0.9873 - miss_rate: 0.3106 - fall_out: 0.0127 - mcc: 0.7468 - val_loss: 0.9341 - val_accuracy: 0.7136 - val_recall: 0.6304 - val_precision: 0.7994 - val_AUROC: 0.9513 - val_AUPRC: 0.7876 - val_f1_score: 0.7049 - val_balanced_accuracy: 0.8064 - val_specificity: 0.9824 - val_miss_rate: 0.3696 - val_fall_out: 0.0176 - val_mcc: 0.6821
250/250 [==============================] - 2s 9ms/step - loss: 0.4096 - accuracy: 0.8825 - recall: 0.7980 - precision: 0.9395 - AUROC: 0.9916 - AUPRC: 0.9491 - f1_score: 0.8630 - balanced_accuracy: 0.8961 - specificity: 0.9943 - miss_rate: 0.2020 - fall_out: 0.0057 - mcc: 0.8526
63/63 [==============================] - 1s 9ms/step - loss: 0.9341 - accuracy: 0.7126 - recall: 0.6304 - precision: 0.7994 - AUROC: 0.9513 - AUPRC: 0.7875 - f1_score: 0.7049 - balanced_accuracy: 0.8064 - specificity: 0.9824 - miss_rate: 0.3696 - fall_out: 0.0176 - mcc: 0.6821
2it [02:22, 71.85s/it]
-- HOLDOUT 3
Model: "CNN_S_3s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_96 (Conv2D) (None, 100, 130, 128) 1280
max_pooling2d_73 (MaxPoolin (None, 50, 65, 128) 0
g2D)
conv2d_97 (Conv2D) (None, 50, 65, 64) 73792
max_pooling2d_74 (MaxPoolin (None, 25, 33, 64) 0
g2D)
conv2d_98 (Conv2D) (None, 25, 33, 64) 36928
max_pooling2d_75 (MaxPoolin (None, 13, 17, 64) 0
g2D)
conv2d_99 (Conv2D) (None, 13, 17, 128) 131200
flatten_24 (Flatten) (None, 28288) 0
dense_72 (Dense) (None, 128) 3620992
dropout_50 (Dropout) (None, 128) 0
dense_73 (Dense) (None, 128) 16512
dropout_51 (Dropout) (None, 128) 0
dense_74 (Dense) (None, 10) 1290
=================================================================
Total params: 3,881,994
Trainable params: 3,881,994
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 6s 80ms/step - loss: 2.1805 - accuracy: 0.2075 - recall: 0.0223 - precision: 0.5205 - AUROC: 0.6515 - AUPRC: 0.1938 - f1_score: 0.0428 - balanced_accuracy: 0.5100 - specificity: 0.9977 - miss_rate: 0.9777 - fall_out: 0.0023 - mcc: 0.0919 - val_loss: 1.8244 - val_accuracy: 0.3200 - val_recall: 0.1437 - val_precision: 0.7397 - val_AUROC: 0.8054 - val_AUPRC: 0.3876 - val_f1_score: 0.2407 - val_balanced_accuracy: 0.5690 - val_specificity: 0.9944 - val_miss_rate: 0.8563 - val_fall_out: 0.0056 - val_mcc: 0.3001
Epoch 2/100
63/63 [==============================] - 5s 74ms/step - loss: 1.7665 - accuracy: 0.3588 - recall: 0.1465 - precision: 0.6414 - AUROC: 0.8103 - AUPRC: 0.3819 - f1_score: 0.2386 - balanced_accuracy: 0.5687 - specificity: 0.9909 - miss_rate: 0.8535 - fall_out: 0.0091 - mcc: 0.2760 - val_loss: 1.5924 - val_accuracy: 0.4281 - val_recall: 0.1948 - val_precision: 0.7257 - val_AUROC: 0.8625 - val_AUPRC: 0.4729 - val_f1_score: 0.3071 - val_balanced_accuracy: 0.5933 - val_specificity: 0.9918 - val_miss_rate: 0.8052 - val_fall_out: 0.0082 - val_mcc: 0.3464
Epoch 3/100
63/63 [==============================] - 5s 74ms/step - loss: 1.5785 - accuracy: 0.4266 - recall: 0.2093 - precision: 0.6877 - AUROC: 0.8559 - AUPRC: 0.4670 - f1_score: 0.3209 - balanced_accuracy: 0.5994 - specificity: 0.9894 - miss_rate: 0.7907 - fall_out: 0.0106 - mcc: 0.3471 - val_loss: 1.3738 - val_accuracy: 0.5058 - val_recall: 0.2354 - val_precision: 0.8007 - val_AUROC: 0.8996 - val_AUPRC: 0.5735 - val_f1_score: 0.3638 - val_balanced_accuracy: 0.6144 - val_specificity: 0.9935 - val_miss_rate: 0.7646 - val_fall_out: 0.0065 - val_mcc: 0.4065
Epoch 4/100
63/63 [==============================] - 5s 73ms/step - loss: 1.4319 - accuracy: 0.4868 - recall: 0.2678 - precision: 0.7187 - AUROC: 0.8832 - AUPRC: 0.5328 - f1_score: 0.3902 - balanced_accuracy: 0.6281 - specificity: 0.9884 - miss_rate: 0.7322 - fall_out: 0.0116 - mcc: 0.4057 - val_loss: 1.2876 - val_accuracy: 0.5453 - val_recall: 0.3315 - val_precision: 0.7725 - val_AUROC: 0.9066 - val_AUPRC: 0.6077 - val_f1_score: 0.4639 - val_balanced_accuracy: 0.6603 - val_specificity: 0.9892 - val_miss_rate: 0.6685 - val_fall_out: 0.0108 - val_mcc: 0.4746
Epoch 5/100
63/63 [==============================] - 5s 73ms/step - loss: 1.3526 - accuracy: 0.5108 - recall: 0.3115 - precision: 0.7358 - AUROC: 0.8956 - AUPRC: 0.5718 - f1_score: 0.4377 - balanced_accuracy: 0.6495 - specificity: 0.9876 - miss_rate: 0.6885 - fall_out: 0.0124 - mcc: 0.4456 - val_loss: 1.1972 - val_accuracy: 0.5949 - val_recall: 0.3620 - val_precision: 0.7850 - val_AUROC: 0.9190 - val_AUPRC: 0.6509 - val_f1_score: 0.4955 - val_balanced_accuracy: 0.6755 - val_specificity: 0.9890 - val_miss_rate: 0.6380 - val_fall_out: 0.0110 - val_mcc: 0.5021
Epoch 6/100
63/63 [==============================] - 5s 73ms/step - loss: 1.2937 - accuracy: 0.5406 - recall: 0.3428 - precision: 0.7528 - AUROC: 0.9052 - AUPRC: 0.6031 - f1_score: 0.4711 - balanced_accuracy: 0.6651 - specificity: 0.9875 - miss_rate: 0.6572 - fall_out: 0.0125 - mcc: 0.4753 - val_loss: 1.1208 - val_accuracy: 0.6189 - val_recall: 0.4251 - val_precision: 0.8070 - val_AUROC: 0.9292 - val_AUPRC: 0.6894 - val_f1_score: 0.5569 - val_balanced_accuracy: 0.7069 - val_specificity: 0.9887 - val_miss_rate: 0.5749 - val_fall_out: 0.0113 - val_mcc: 0.5558
Epoch 7/100
63/63 [==============================] - 5s 74ms/step - loss: 1.1508 - accuracy: 0.5949 - recall: 0.4147 - precision: 0.7802 - AUROC: 0.9252 - AUPRC: 0.6683 - f1_score: 0.5415 - balanced_accuracy: 0.7009 - specificity: 0.9870 - miss_rate: 0.5853 - fall_out: 0.0130 - mcc: 0.5372 - val_loss: 1.0882 - val_accuracy: 0.6299 - val_recall: 0.4382 - val_precision: 0.8005 - val_AUROC: 0.9334 - val_AUPRC: 0.7001 - val_f1_score: 0.5663 - val_balanced_accuracy: 0.7130 - val_specificity: 0.9879 - val_miss_rate: 0.5618 - val_fall_out: 0.0121 - val_mcc: 0.5619
Epoch 8/100
63/63 [==============================] - 5s 73ms/step - loss: 1.0997 - accuracy: 0.6096 - recall: 0.4419 - precision: 0.7852 - AUROC: 0.9317 - AUPRC: 0.6919 - f1_score: 0.5655 - balanced_accuracy: 0.7142 - specificity: 0.9866 - miss_rate: 0.5581 - fall_out: 0.0134 - mcc: 0.5578 - val_loss: 1.0648 - val_accuracy: 0.6405 - val_recall: 0.4702 - val_precision: 0.8074 - val_AUROC: 0.9346 - val_AUPRC: 0.7169 - val_f1_score: 0.5943 - val_balanced_accuracy: 0.7289 - val_specificity: 0.9875 - val_miss_rate: 0.5298 - val_fall_out: 0.0125 - val_mcc: 0.5864
Epoch 9/100
63/63 [==============================] - 5s 73ms/step - loss: 1.0050 - accuracy: 0.6459 - recall: 0.4955 - precision: 0.8010 - AUROC: 0.9427 - AUPRC: 0.7314 - f1_score: 0.6122 - balanced_accuracy: 0.7409 - specificity: 0.9863 - miss_rate: 0.5045 - fall_out: 0.0137 - mcc: 0.6000 - val_loss: 0.9430 - val_accuracy: 0.6755 - val_recall: 0.5578 - val_precision: 0.8084 - val_AUROC: 0.9490 - val_AUPRC: 0.7613 - val_f1_score: 0.6601 - val_balanced_accuracy: 0.7716 - val_specificity: 0.9853 - val_miss_rate: 0.4422 - val_fall_out: 0.0147 - val_mcc: 0.6429
Epoch 10/100
63/63 [==============================] - 5s 73ms/step - loss: 0.9100 - accuracy: 0.6776 - recall: 0.5470 - precision: 0.8192 - AUROC: 0.9529 - AUPRC: 0.7738 - f1_score: 0.6560 - balanced_accuracy: 0.7668 - specificity: 0.9866 - miss_rate: 0.4530 - fall_out: 0.0134 - mcc: 0.6412 - val_loss: 0.9213 - val_accuracy: 0.6830 - val_recall: 0.5598 - val_precision: 0.8368 - val_AUROC: 0.9506 - val_AUPRC: 0.7790 - val_f1_score: 0.6709 - val_balanced_accuracy: 0.7739 - val_specificity: 0.9879 - val_miss_rate: 0.4402 - val_fall_out: 0.0121 - val_mcc: 0.6576
Epoch 11/100
63/63 [==============================] - 5s 74ms/step - loss: 0.8414 - accuracy: 0.7112 - recall: 0.5855 - precision: 0.8398 - AUROC: 0.9597 - AUPRC: 0.8022 - f1_score: 0.6900 - balanced_accuracy: 0.7866 - specificity: 0.9876 - miss_rate: 0.4145 - fall_out: 0.0124 - mcc: 0.6751 - val_loss: 0.9017 - val_accuracy: 0.7051 - val_recall: 0.5949 - val_precision: 0.8199 - val_AUROC: 0.9514 - val_AUPRC: 0.7910 - val_f1_score: 0.6895 - val_balanced_accuracy: 0.7902 - val_specificity: 0.9855 - val_miss_rate: 0.4051 - val_fall_out: 0.0145 - val_mcc: 0.6712
Epoch 12/100
63/63 [==============================] - 5s 75ms/step - loss: 0.7909 - accuracy: 0.7267 - recall: 0.6144 - precision: 0.8421 - AUROC: 0.9641 - AUPRC: 0.8208 - f1_score: 0.7104 - balanced_accuracy: 0.8008 - specificity: 0.9872 - miss_rate: 0.3856 - fall_out: 0.0128 - mcc: 0.6939 - val_loss: 0.8921 - val_accuracy: 0.7121 - val_recall: 0.6149 - val_precision: 0.8079 - val_AUROC: 0.9530 - val_AUPRC: 0.7976 - val_f1_score: 0.6983 - val_balanced_accuracy: 0.7993 - val_specificity: 0.9838 - val_miss_rate: 0.3851 - val_fall_out: 0.0162 - val_mcc: 0.6773
Epoch 13/100
63/63 [==============================] - 5s 73ms/step - loss: 0.7169 - accuracy: 0.7525 - recall: 0.6498 - precision: 0.8575 - AUROC: 0.9702 - AUPRC: 0.8476 - f1_score: 0.7393 - balanced_accuracy: 0.8189 - specificity: 0.9880 - miss_rate: 0.3502 - fall_out: 0.0120 - mcc: 0.7230 - val_loss: 0.8502 - val_accuracy: 0.7261 - val_recall: 0.6355 - val_precision: 0.8278 - val_AUROC: 0.9577 - val_AUPRC: 0.8114 - val_f1_score: 0.7190 - val_balanced_accuracy: 0.8104 - val_specificity: 0.9853 - val_miss_rate: 0.3645 - val_fall_out: 0.0147 - val_mcc: 0.6995
Epoch 14/100
63/63 [==============================] - 5s 74ms/step - loss: 0.6847 - accuracy: 0.7643 - recall: 0.6657 - precision: 0.8598 - AUROC: 0.9725 - AUPRC: 0.8578 - f1_score: 0.7504 - balanced_accuracy: 0.8268 - specificity: 0.9879 - miss_rate: 0.3343 - fall_out: 0.0121 - mcc: 0.7337 - val_loss: 0.9085 - val_accuracy: 0.7086 - val_recall: 0.6420 - val_precision: 0.8088 - val_AUROC: 0.9535 - val_AUPRC: 0.7983 - val_f1_score: 0.7158 - val_balanced_accuracy: 0.8126 - val_specificity: 0.9831 - val_miss_rate: 0.3580 - val_fall_out: 0.0169 - val_mcc: 0.6938
Epoch 15/100
63/63 [==============================] - 5s 73ms/step - loss: 0.6168 - accuracy: 0.7943 - recall: 0.7099 - precision: 0.8727 - AUROC: 0.9771 - AUPRC: 0.8792 - f1_score: 0.7829 - balanced_accuracy: 0.8492 - specificity: 0.9885 - miss_rate: 0.2901 - fall_out: 0.0115 - mcc: 0.7664 - val_loss: 0.8906 - val_accuracy: 0.7166 - val_recall: 0.6450 - val_precision: 0.7975 - val_AUROC: 0.9536 - val_AUPRC: 0.7998 - val_f1_score: 0.7132 - val_balanced_accuracy: 0.8134 - val_specificity: 0.9818 - val_miss_rate: 0.3550 - val_fall_out: 0.0182 - val_mcc: 0.6897
250/250 [==============================] - 2s 9ms/step - loss: 0.3120 - accuracy: 0.9153 - recall: 0.8540 - precision: 0.9618 - AUROC: 0.9959 - AUPRC: 0.9725 - f1_score: 0.9047 - balanced_accuracy: 0.9251 - specificity: 0.9962 - miss_rate: 0.1460 - fall_out: 0.0038 - mcc: 0.8967
63/63 [==============================] - 1s 9ms/step - loss: 0.8906 - accuracy: 0.7161 - recall: 0.6450 - precision: 0.7975 - AUROC: 0.9536 - AUPRC: 0.7998 - f1_score: 0.7132 - balanced_accuracy: 0.8134 - specificity: 0.9818 - miss_rate: 0.3550 - fall_out: 0.0182 - mcc: 0.6897
3it [03:36, 72.78s/it]
-- HOLDOUT 4
Model: "CNN_S_3s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_100 (Conv2D) (None, 100, 130, 128) 1280
max_pooling2d_76 (MaxPoolin (None, 50, 65, 128) 0
g2D)
conv2d_101 (Conv2D) (None, 50, 65, 64) 73792
max_pooling2d_77 (MaxPoolin (None, 25, 33, 64) 0
g2D)
conv2d_102 (Conv2D) (None, 25, 33, 64) 36928
max_pooling2d_78 (MaxPoolin (None, 13, 17, 64) 0
g2D)
conv2d_103 (Conv2D) (None, 13, 17, 128) 131200
flatten_25 (Flatten) (None, 28288) 0
dense_75 (Dense) (None, 128) 3620992
dropout_52 (Dropout) (None, 128) 0
dense_76 (Dense) (None, 128) 16512
dropout_53 (Dropout) (None, 128) 0
dense_77 (Dense) (None, 10) 1290
=================================================================
Total params: 3,881,994
Trainable params: 3,881,994
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 8s 82ms/step - loss: 2.1897 - accuracy: 0.1971 - recall: 0.0230 - precision: 0.5154 - AUROC: 0.6421 - AUPRC: 0.1903 - f1_score: 0.0441 - balanced_accuracy: 0.5103 - specificity: 0.9976 - miss_rate: 0.9770 - fall_out: 0.0024 - mcc: 0.0928 - val_loss: 1.7672 - val_accuracy: 0.3866 - val_recall: 0.0541 - val_precision: 0.8640 - val_AUROC: 0.8343 - val_AUPRC: 0.4361 - val_f1_score: 0.1018 - val_balanced_accuracy: 0.5266 - val_specificity: 0.9991 - val_miss_rate: 0.9459 - val_fall_out: 9.4586e-04 - val_mcc: 0.2021
Epoch 2/100
63/63 [==============================] - 5s 73ms/step - loss: 1.8130 - accuracy: 0.3424 - recall: 0.1258 - precision: 0.6649 - AUROC: 0.7953 - AUPRC: 0.3624 - f1_score: 0.2115 - balanced_accuracy: 0.5594 - specificity: 0.9930 - miss_rate: 0.8742 - fall_out: 0.0070 - mcc: 0.2614 - val_loss: 1.4597 - val_accuracy: 0.4627 - val_recall: 0.2539 - val_precision: 0.7434 - val_AUROC: 0.8830 - val_AUPRC: 0.5246 - val_f1_score: 0.3785 - val_balanced_accuracy: 0.6221 - val_specificity: 0.9903 - val_miss_rate: 0.7461 - val_fall_out: 0.0097 - val_mcc: 0.4033
Epoch 3/100
63/63 [==============================] - 5s 74ms/step - loss: 1.6081 - accuracy: 0.4127 - recall: 0.1924 - precision: 0.6879 - AUROC: 0.8489 - AUPRC: 0.4491 - f1_score: 0.3007 - balanced_accuracy: 0.5913 - specificity: 0.9903 - miss_rate: 0.8076 - fall_out: 0.0097 - mcc: 0.3324 - val_loss: 1.3599 - val_accuracy: 0.5133 - val_recall: 0.2559 - val_precision: 0.8309 - val_AUROC: 0.9046 - val_AUPRC: 0.5889 - val_f1_score: 0.3913 - val_balanced_accuracy: 0.6250 - val_specificity: 0.9942 - val_miss_rate: 0.7441 - val_fall_out: 0.0058 - val_mcc: 0.4343
Epoch 4/100
63/63 [==============================] - 5s 73ms/step - loss: 1.4803 - accuracy: 0.4600 - recall: 0.2437 - precision: 0.7069 - AUROC: 0.8736 - AUPRC: 0.5117 - f1_score: 0.3625 - balanced_accuracy: 0.6163 - specificity: 0.9888 - miss_rate: 0.7563 - fall_out: 0.0112 - mcc: 0.3823 - val_loss: 1.3195 - val_accuracy: 0.5428 - val_recall: 0.3230 - val_precision: 0.7264 - val_AUROC: 0.9025 - val_AUPRC: 0.5936 - val_f1_score: 0.4471 - val_balanced_accuracy: 0.6547 - val_specificity: 0.9865 - val_miss_rate: 0.6770 - val_fall_out: 0.0135 - val_mcc: 0.4504
Epoch 5/100
63/63 [==============================] - 5s 76ms/step - loss: 1.3623 - accuracy: 0.5130 - recall: 0.2993 - precision: 0.7411 - AUROC: 0.8948 - AUPRC: 0.5701 - f1_score: 0.4264 - balanced_accuracy: 0.6439 - specificity: 0.9884 - miss_rate: 0.7007 - fall_out: 0.0116 - mcc: 0.4384 - val_loss: 1.1393 - val_accuracy: 0.5889 - val_recall: 0.3851 - val_precision: 0.8019 - val_AUROC: 0.9276 - val_AUPRC: 0.6763 - val_f1_score: 0.5203 - val_balanced_accuracy: 0.6873 - val_specificity: 0.9894 - val_miss_rate: 0.6149 - val_fall_out: 0.0106 - val_mcc: 0.5255
Epoch 6/100
63/63 [==============================] - 5s 74ms/step - loss: 1.2662 - accuracy: 0.5610 - recall: 0.3572 - precision: 0.7630 - AUROC: 0.9091 - AUPRC: 0.6159 - f1_score: 0.4866 - balanced_accuracy: 0.6724 - specificity: 0.9877 - miss_rate: 0.6428 - fall_out: 0.0123 - mcc: 0.4898 - val_loss: 1.1105 - val_accuracy: 0.6109 - val_recall: 0.4106 - val_precision: 0.8316 - val_AUROC: 0.9324 - val_AUPRC: 0.6960 - val_f1_score: 0.5498 - val_balanced_accuracy: 0.7007 - val_specificity: 0.9908 - val_miss_rate: 0.5894 - val_fall_out: 0.0092 - val_mcc: 0.5558
Epoch 7/100
63/63 [==============================] - 5s 73ms/step - loss: 1.1755 - accuracy: 0.5894 - recall: 0.3990 - precision: 0.7683 - AUROC: 0.9221 - AUPRC: 0.6581 - f1_score: 0.5253 - balanced_accuracy: 0.6928 - specificity: 0.9866 - miss_rate: 0.6010 - fall_out: 0.0134 - mcc: 0.5214 - val_loss: 1.0700 - val_accuracy: 0.6219 - val_recall: 0.4552 - val_precision: 0.7918 - val_AUROC: 0.9356 - val_AUPRC: 0.7045 - val_f1_score: 0.5781 - val_balanced_accuracy: 0.7209 - val_specificity: 0.9867 - val_miss_rate: 0.5448 - val_fall_out: 0.0133 - val_mcc: 0.5695
Epoch 8/100
63/63 [==============================] - 5s 74ms/step - loss: 1.0937 - accuracy: 0.6224 - recall: 0.4542 - precision: 0.7908 - AUROC: 0.9323 - AUPRC: 0.6959 - f1_score: 0.5770 - balanced_accuracy: 0.7204 - specificity: 0.9867 - miss_rate: 0.5458 - fall_out: 0.0133 - mcc: 0.5684 - val_loss: 0.9726 - val_accuracy: 0.6690 - val_recall: 0.5108 - val_precision: 0.8430 - val_AUROC: 0.9468 - val_AUPRC: 0.7601 - val_f1_score: 0.6361 - val_balanced_accuracy: 0.7501 - val_specificity: 0.9894 - val_miss_rate: 0.4892 - val_fall_out: 0.0106 - val_mcc: 0.6290
Epoch 9/100
63/63 [==============================] - 5s 74ms/step - loss: 1.0016 - accuracy: 0.6537 - recall: 0.5031 - precision: 0.8104 - AUROC: 0.9436 - AUPRC: 0.7387 - f1_score: 0.6208 - balanced_accuracy: 0.7450 - specificity: 0.9869 - miss_rate: 0.4969 - fall_out: 0.0131 - mcc: 0.6092 - val_loss: 0.9974 - val_accuracy: 0.6685 - val_recall: 0.4767 - val_precision: 0.8373 - val_AUROC: 0.9441 - val_AUPRC: 0.7490 - val_f1_score: 0.6075 - val_balanced_accuracy: 0.7332 - val_specificity: 0.9897 - val_miss_rate: 0.5233 - val_fall_out: 0.0103 - val_mcc: 0.6039
Epoch 10/100
63/63 [==============================] - 5s 74ms/step - loss: 0.9416 - accuracy: 0.6731 - recall: 0.5366 - precision: 0.8185 - AUROC: 0.9497 - AUPRC: 0.7606 - f1_score: 0.6482 - balanced_accuracy: 0.7617 - specificity: 0.9868 - miss_rate: 0.4634 - fall_out: 0.0132 - mcc: 0.6344 - val_loss: 0.9023 - val_accuracy: 0.6980 - val_recall: 0.5909 - val_precision: 0.8138 - val_AUROC: 0.9528 - val_AUPRC: 0.7837 - val_f1_score: 0.6847 - val_balanced_accuracy: 0.7879 - val_specificity: 0.9850 - val_miss_rate: 0.4091 - val_fall_out: 0.0150 - val_mcc: 0.6658
Epoch 11/100
63/63 [==============================] - 5s 73ms/step - loss: 0.8404 - accuracy: 0.7087 - recall: 0.5817 - precision: 0.8332 - AUROC: 0.9599 - AUPRC: 0.8004 - f1_score: 0.6851 - balanced_accuracy: 0.7844 - specificity: 0.9871 - miss_rate: 0.4183 - fall_out: 0.0129 - mcc: 0.6695 - val_loss: 0.8834 - val_accuracy: 0.7016 - val_recall: 0.6074 - val_precision: 0.8146 - val_AUROC: 0.9545 - val_AUPRC: 0.7900 - val_f1_score: 0.6959 - val_balanced_accuracy: 0.7960 - val_specificity: 0.9846 - val_miss_rate: 0.3926 - val_fall_out: 0.0154 - val_mcc: 0.6762
Epoch 12/100
63/63 [==============================] - 5s 73ms/step - loss: 0.7758 - accuracy: 0.7318 - recall: 0.6217 - precision: 0.8468 - AUROC: 0.9655 - AUPRC: 0.8253 - f1_score: 0.7170 - balanced_accuracy: 0.8046 - specificity: 0.9875 - miss_rate: 0.3783 - fall_out: 0.0125 - mcc: 0.7007 - val_loss: 0.8770 - val_accuracy: 0.7026 - val_recall: 0.6159 - val_precision: 0.8039 - val_AUROC: 0.9553 - val_AUPRC: 0.7940 - val_f1_score: 0.6975 - val_balanced_accuracy: 0.7996 - val_specificity: 0.9833 - val_miss_rate: 0.3841 - val_fall_out: 0.0167 - val_mcc: 0.6759
Epoch 13/100
63/63 [==============================] - 5s 73ms/step - loss: 0.7489 - accuracy: 0.7396 - recall: 0.6359 - precision: 0.8526 - AUROC: 0.9679 - AUPRC: 0.8346 - f1_score: 0.7285 - balanced_accuracy: 0.8118 - specificity: 0.9878 - miss_rate: 0.3641 - fall_out: 0.0122 - mcc: 0.7122 - val_loss: 0.8659 - val_accuracy: 0.7196 - val_recall: 0.6340 - val_precision: 0.8242 - val_AUROC: 0.9569 - val_AUPRC: 0.8075 - val_f1_score: 0.7167 - val_balanced_accuracy: 0.8095 - val_specificity: 0.9850 - val_miss_rate: 0.3660 - val_fall_out: 0.0150 - val_mcc: 0.6968
Epoch 14/100
63/63 [==============================] - 5s 75ms/step - loss: 0.6833 - accuracy: 0.7689 - recall: 0.6735 - precision: 0.8609 - AUROC: 0.9727 - AUPRC: 0.8584 - f1_score: 0.7557 - balanced_accuracy: 0.8307 - specificity: 0.9879 - miss_rate: 0.3265 - fall_out: 0.0121 - mcc: 0.7389 - val_loss: 0.8370 - val_accuracy: 0.7331 - val_recall: 0.6400 - val_precision: 0.8208 - val_AUROC: 0.9577 - val_AUPRC: 0.8129 - val_f1_score: 0.7192 - val_balanced_accuracy: 0.8122 - val_specificity: 0.9845 - val_miss_rate: 0.3600 - val_fall_out: 0.0155 - val_mcc: 0.6987
Epoch 15/100
63/63 [==============================] - 5s 75ms/step - loss: 0.6244 - accuracy: 0.7784 - recall: 0.6986 - precision: 0.8728 - AUROC: 0.9771 - AUPRC: 0.8776 - f1_score: 0.7761 - balanced_accuracy: 0.8437 - specificity: 0.9887 - miss_rate: 0.3014 - fall_out: 0.0113 - mcc: 0.7599 - val_loss: 0.8680 - val_accuracy: 0.7291 - val_recall: 0.6790 - val_precision: 0.8047 - val_AUROC: 0.9587 - val_AUPRC: 0.8177 - val_f1_score: 0.7366 - val_balanced_accuracy: 0.8304 - val_specificity: 0.9817 - val_miss_rate: 0.3210 - val_fall_out: 0.0183 - val_mcc: 0.7131
Epoch 16/100
63/63 [==============================] - 5s 74ms/step - loss: 0.5912 - accuracy: 0.7965 - recall: 0.7207 - precision: 0.8746 - AUROC: 0.9793 - AUPRC: 0.8871 - f1_score: 0.7902 - balanced_accuracy: 0.8546 - specificity: 0.9885 - miss_rate: 0.2793 - fall_out: 0.0115 - mcc: 0.7737 - val_loss: 0.8784 - val_accuracy: 0.7356 - val_recall: 0.6725 - val_precision: 0.7999 - val_AUROC: 0.9557 - val_AUPRC: 0.8126 - val_f1_score: 0.7307 - val_balanced_accuracy: 0.8269 - val_specificity: 0.9813 - val_miss_rate: 0.3275 - val_fall_out: 0.0187 - val_mcc: 0.7068
250/250 [==============================] - 2s 9ms/step - loss: 0.2781 - accuracy: 0.9162 - recall: 0.8724 - precision: 0.9532 - AUROC: 0.9961 - AUPRC: 0.9738 - f1_score: 0.9110 - balanced_accuracy: 0.9338 - specificity: 0.9952 - miss_rate: 0.1276 - fall_out: 0.0048 - mcc: 0.9027
63/63 [==============================] - 1s 9ms/step - loss: 0.8785 - accuracy: 0.7356 - recall: 0.6725 - precision: 0.7999 - AUROC: 0.9557 - AUPRC: 0.8126 - f1_score: 0.7307 - balanced_accuracy: 0.8269 - specificity: 0.9813 - miss_rate: 0.3275 - fall_out: 0.0187 - mcc: 0.7068
4it [04:57, 75.82s/it]
-- HOLDOUT 5
Model: "CNN_S_3s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_104 (Conv2D) (None, 100, 130, 128) 1280
max_pooling2d_79 (MaxPoolin (None, 50, 65, 128) 0
g2D)
conv2d_105 (Conv2D) (None, 50, 65, 64) 73792
max_pooling2d_80 (MaxPoolin (None, 25, 33, 64) 0
g2D)
conv2d_106 (Conv2D) (None, 25, 33, 64) 36928
max_pooling2d_81 (MaxPoolin (None, 13, 17, 64) 0
g2D)
conv2d_107 (Conv2D) (None, 13, 17, 128) 131200
flatten_26 (Flatten) (None, 28288) 0
dense_78 (Dense) (None, 128) 3620992
dropout_54 (Dropout) (None, 128) 0
dense_79 (Dense) (None, 128) 16512
dropout_55 (Dropout) (None, 128) 0
dense_80 (Dense) (None, 10) 1290
=================================================================
Total params: 3,881,994
Trainable params: 3,881,994
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 6s 82ms/step - loss: 2.2000 - accuracy: 0.2113 - recall: 0.0296 - precision: 0.5413 - AUROC: 0.6499 - AUPRC: 0.1959 - f1_score: 0.0561 - balanced_accuracy: 0.5134 - specificity: 0.9972 - miss_rate: 0.9704 - fall_out: 0.0028 - mcc: 0.1090 - val_loss: 1.8376 - val_accuracy: 0.3285 - val_recall: 0.0901 - val_precision: 0.7469 - val_AUROC: 0.7953 - val_AUPRC: 0.3699 - val_f1_score: 0.1609 - val_balanced_accuracy: 0.5434 - val_specificity: 0.9966 - val_miss_rate: 0.9099 - val_fall_out: 0.0034 - val_mcc: 0.2383
Epoch 2/100
63/63 [==============================] - 5s 74ms/step - loss: 1.7931 - accuracy: 0.3471 - recall: 0.1374 - precision: 0.6640 - AUROC: 0.7999 - AUPRC: 0.3726 - f1_score: 0.2277 - balanced_accuracy: 0.5648 - specificity: 0.9923 - miss_rate: 0.8626 - fall_out: 0.0077 - mcc: 0.2733 - val_loss: 1.5191 - val_accuracy: 0.4652 - val_recall: 0.1868 - val_precision: 0.7430 - val_AUROC: 0.8775 - val_AUPRC: 0.5018 - val_f1_score: 0.2985 - val_balanced_accuracy: 0.5898 - val_specificity: 0.9928 - val_miss_rate: 0.8132 - val_fall_out: 0.0072 - val_mcc: 0.3442
Epoch 3/100
63/63 [==============================] - 5s 74ms/step - loss: 1.5768 - accuracy: 0.4359 - recall: 0.2115 - precision: 0.6968 - AUROC: 0.8557 - AUPRC: 0.4698 - f1_score: 0.3246 - balanced_accuracy: 0.6007 - specificity: 0.9898 - miss_rate: 0.7885 - fall_out: 0.0102 - mcc: 0.3520 - val_loss: 1.3110 - val_accuracy: 0.5478 - val_recall: 0.2914 - val_precision: 0.7833 - val_AUROC: 0.9050 - val_AUPRC: 0.5978 - val_f1_score: 0.4248 - val_balanced_accuracy: 0.6412 - val_specificity: 0.9910 - val_miss_rate: 0.7086 - val_fall_out: 0.0090 - val_mcc: 0.4478
Epoch 4/100
63/63 [==============================] - 5s 74ms/step - loss: 1.4168 - accuracy: 0.4934 - recall: 0.2831 - precision: 0.7288 - AUROC: 0.8853 - AUPRC: 0.5421 - f1_score: 0.4078 - balanced_accuracy: 0.6357 - specificity: 0.9883 - miss_rate: 0.7169 - fall_out: 0.0117 - mcc: 0.4213 - val_loss: 1.2610 - val_accuracy: 0.5333 - val_recall: 0.3045 - val_precision: 0.7896 - val_AUROC: 0.9142 - val_AUPRC: 0.6155 - val_f1_score: 0.4395 - val_balanced_accuracy: 0.6477 - val_specificity: 0.9910 - val_miss_rate: 0.6955 - val_fall_out: 0.0090 - val_mcc: 0.4603
Epoch 5/100
63/63 [==============================] - 5s 73ms/step - loss: 1.3172 - accuracy: 0.5374 - recall: 0.3358 - precision: 0.7656 - AUROC: 0.9015 - AUPRC: 0.6000 - f1_score: 0.4668 - balanced_accuracy: 0.6622 - specificity: 0.9886 - miss_rate: 0.6642 - fall_out: 0.0114 - mcc: 0.4752 - val_loss: 1.3218 - val_accuracy: 0.5238 - val_recall: 0.3100 - val_precision: 0.7728 - val_AUROC: 0.9010 - val_AUPRC: 0.5966 - val_f1_score: 0.4425 - val_balanced_accuracy: 0.6499 - val_specificity: 0.9899 - val_miss_rate: 0.6900 - val_fall_out: 0.0101 - val_mcc: 0.4584
Epoch 6/100
63/63 [==============================] - 5s 75ms/step - loss: 1.2127 - accuracy: 0.5765 - recall: 0.3809 - precision: 0.7705 - AUROC: 0.9169 - AUPRC: 0.6421 - f1_score: 0.5098 - balanced_accuracy: 0.6841 - specificity: 0.9874 - miss_rate: 0.6191 - fall_out: 0.0126 - mcc: 0.5097 - val_loss: 1.0876 - val_accuracy: 0.6174 - val_recall: 0.4357 - val_precision: 0.8131 - val_AUROC: 0.9341 - val_AUPRC: 0.7044 - val_f1_score: 0.5673 - val_balanced_accuracy: 0.7123 - val_specificity: 0.9889 - val_miss_rate: 0.5643 - val_fall_out: 0.0111 - val_mcc: 0.5656
Epoch 7/100
63/63 [==============================] - 5s 75ms/step - loss: 1.1206 - accuracy: 0.6121 - recall: 0.4366 - precision: 0.7802 - AUROC: 0.9289 - AUPRC: 0.6840 - f1_score: 0.5599 - balanced_accuracy: 0.7115 - specificity: 0.9863 - miss_rate: 0.5634 - fall_out: 0.0137 - mcc: 0.5520 - val_loss: 0.9482 - val_accuracy: 0.6760 - val_recall: 0.5268 - val_precision: 0.8225 - val_AUROC: 0.9486 - val_AUPRC: 0.7655 - val_f1_score: 0.6422 - val_balanced_accuracy: 0.7571 - val_specificity: 0.9874 - val_miss_rate: 0.4732 - val_fall_out: 0.0126 - val_mcc: 0.6300
Epoch 8/100
63/63 [==============================] - 5s 74ms/step - loss: 1.0286 - accuracy: 0.6465 - recall: 0.4871 - precision: 0.8010 - AUROC: 0.9401 - AUPRC: 0.7269 - f1_score: 0.6058 - balanced_accuracy: 0.7368 - specificity: 0.9866 - miss_rate: 0.5129 - fall_out: 0.0134 - mcc: 0.5946 - val_loss: 0.9851 - val_accuracy: 0.6655 - val_recall: 0.4672 - val_precision: 0.8639 - val_AUROC: 0.9476 - val_AUPRC: 0.7572 - val_f1_score: 0.6064 - val_balanced_accuracy: 0.7295 - val_specificity: 0.9918 - val_miss_rate: 0.5328 - val_fall_out: 0.0082 - val_mcc: 0.6088
Epoch 9/100
63/63 [==============================] - 5s 74ms/step - loss: 0.9710 - accuracy: 0.6723 - recall: 0.5167 - precision: 0.8091 - AUROC: 0.9466 - AUPRC: 0.7491 - f1_score: 0.6306 - balanced_accuracy: 0.7516 - specificity: 0.9865 - miss_rate: 0.4833 - fall_out: 0.0135 - mcc: 0.6173 - val_loss: 0.9199 - val_accuracy: 0.6865 - val_recall: 0.5543 - val_precision: 0.8146 - val_AUROC: 0.9517 - val_AUPRC: 0.7728 - val_f1_score: 0.6597 - val_balanced_accuracy: 0.7702 - val_specificity: 0.9860 - val_miss_rate: 0.4457 - val_fall_out: 0.0140 - val_mcc: 0.6436
Epoch 10/100
63/63 [==============================] - 5s 75ms/step - loss: 0.8544 - accuracy: 0.7108 - recall: 0.5810 - precision: 0.8329 - AUROC: 0.9581 - AUPRC: 0.7965 - f1_score: 0.6845 - balanced_accuracy: 0.7840 - specificity: 0.9870 - miss_rate: 0.4190 - fall_out: 0.0130 - mcc: 0.6690 - val_loss: 0.8752 - val_accuracy: 0.7026 - val_recall: 0.5894 - val_precision: 0.8134 - val_AUROC: 0.9560 - val_AUPRC: 0.7906 - val_f1_score: 0.6835 - val_balanced_accuracy: 0.7872 - val_specificity: 0.9850 - val_miss_rate: 0.4106 - val_fall_out: 0.0150 - val_mcc: 0.6647
Epoch 11/100
63/63 [==============================] - 5s 74ms/step - loss: 0.8093 - accuracy: 0.7233 - recall: 0.6129 - precision: 0.8357 - AUROC: 0.9621 - AUPRC: 0.8146 - f1_score: 0.7071 - balanced_accuracy: 0.7997 - specificity: 0.9866 - miss_rate: 0.3871 - fall_out: 0.0134 - mcc: 0.6899 - val_loss: 0.8893 - val_accuracy: 0.7051 - val_recall: 0.5869 - val_precision: 0.8277 - val_AUROC: 0.9539 - val_AUPRC: 0.7877 - val_f1_score: 0.6868 - val_balanced_accuracy: 0.7867 - val_specificity: 0.9864 - val_miss_rate: 0.4131 - val_fall_out: 0.0136 - val_mcc: 0.6701
Epoch 12/100
63/63 [==============================] - 5s 73ms/step - loss: 0.7286 - accuracy: 0.7534 - recall: 0.6495 - precision: 0.8619 - AUROC: 0.9692 - AUPRC: 0.8435 - f1_score: 0.7408 - balanced_accuracy: 0.8190 - specificity: 0.9884 - miss_rate: 0.3505 - fall_out: 0.0116 - mcc: 0.7250 - val_loss: 0.8912 - val_accuracy: 0.7036 - val_recall: 0.6385 - val_precision: 0.8009 - val_AUROC: 0.9547 - val_AUPRC: 0.7949 - val_f1_score: 0.7105 - val_balanced_accuracy: 0.8104 - val_specificity: 0.9824 - val_miss_rate: 0.3615 - val_fall_out: 0.0176 - val_mcc: 0.6876
250/250 [==============================] - 2s 9ms/step - loss: 0.4592 - accuracy: 0.8540 - recall: 0.7687 - precision: 0.9234 - AUROC: 0.9885 - AUPRC: 0.9326 - f1_score: 0.8390 - balanced_accuracy: 0.8808 - specificity: 0.9929 - miss_rate: 0.2313 - fall_out: 0.0071 - mcc: 0.8271
63/63 [==============================] - 1s 9ms/step - loss: 0.8912 - accuracy: 0.7036 - recall: 0.6380 - precision: 0.8008 - AUROC: 0.9547 - AUPRC: 0.7949 - f1_score: 0.7101 - balanced_accuracy: 0.8102 - specificity: 0.9824 - miss_rate: 0.3620 - fall_out: 0.0176 - mcc: 0.6873
5it [05:57, 70.31s/it]
-- HOLDOUT 6
Model: "CNN_S_3s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_108 (Conv2D) (None, 100, 130, 128) 1280
max_pooling2d_82 (MaxPoolin (None, 50, 65, 128) 0
g2D)
conv2d_109 (Conv2D) (None, 50, 65, 64) 73792
max_pooling2d_83 (MaxPoolin (None, 25, 33, 64) 0
g2D)
conv2d_110 (Conv2D) (None, 25, 33, 64) 36928
max_pooling2d_84 (MaxPoolin (None, 13, 17, 64) 0
g2D)
conv2d_111 (Conv2D) (None, 13, 17, 128) 131200
flatten_27 (Flatten) (None, 28288) 0
dense_81 (Dense) (None, 128) 3620992
dropout_56 (Dropout) (None, 128) 0
dense_82 (Dense) (None, 128) 16512
dropout_57 (Dropout) (None, 128) 0
dense_83 (Dense) (None, 10) 1290
=================================================================
Total params: 3,881,994
Trainable params: 3,881,994
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 6s 81ms/step - loss: 2.1104 - accuracy: 0.2241 - recall: 0.0473 - precision: 0.5962 - AUROC: 0.6812 - AUPRC: 0.2314 - f1_score: 0.0877 - balanced_accuracy: 0.5219 - specificity: 0.9964 - miss_rate: 0.9527 - fall_out: 0.0036 - mcc: 0.1480 - val_loss: 1.7348 - val_accuracy: 0.3701 - val_recall: 0.2083 - val_precision: 0.6842 - val_AUROC: 0.8236 - val_AUPRC: 0.4245 - val_f1_score: 0.3194 - val_balanced_accuracy: 0.5988 - val_specificity: 0.9893 - val_miss_rate: 0.7917 - val_fall_out: 0.0107 - val_mcc: 0.3451
Epoch 2/100
63/63 [==============================] - 5s 77ms/step - loss: 1.7445 - accuracy: 0.3662 - recall: 0.1566 - precision: 0.6838 - AUROC: 0.8166 - AUPRC: 0.3986 - f1_score: 0.2548 - balanced_accuracy: 0.5743 - specificity: 0.9920 - miss_rate: 0.8434 - fall_out: 0.0080 - mcc: 0.2979 - val_loss: 1.4883 - val_accuracy: 0.4677 - val_recall: 0.2344 - val_precision: 0.7905 - val_AUROC: 0.8763 - val_AUPRC: 0.5266 - val_f1_score: 0.3615 - val_balanced_accuracy: 0.6137 - val_specificity: 0.9931 - val_miss_rate: 0.7656 - val_fall_out: 0.0069 - val_mcc: 0.4023
Epoch 3/100
63/63 [==============================] - 5s 76ms/step - loss: 1.5465 - accuracy: 0.4342 - recall: 0.2236 - precision: 0.7117 - AUROC: 0.8609 - AUPRC: 0.4809 - f1_score: 0.3403 - balanced_accuracy: 0.6068 - specificity: 0.9899 - miss_rate: 0.7764 - fall_out: 0.0101 - mcc: 0.3672 - val_loss: 1.4119 - val_accuracy: 0.5098 - val_recall: 0.2744 - val_precision: 0.7527 - val_AUROC: 0.8872 - val_AUPRC: 0.5606 - val_f1_score: 0.4022 - val_balanced_accuracy: 0.6322 - val_specificity: 0.9900 - val_miss_rate: 0.7256 - val_fall_out: 0.0100 - val_mcc: 0.4232
Epoch 4/100
63/63 [==============================] - 5s 76ms/step - loss: 1.4023 - accuracy: 0.4901 - recall: 0.2794 - precision: 0.7227 - AUROC: 0.8883 - AUPRC: 0.5460 - f1_score: 0.4030 - balanced_accuracy: 0.6338 - specificity: 0.9881 - miss_rate: 0.7206 - fall_out: 0.0119 - mcc: 0.4163 - val_loss: 1.2217 - val_accuracy: 0.5859 - val_recall: 0.3365 - val_precision: 0.8195 - val_AUROC: 0.9173 - val_AUPRC: 0.6427 - val_f1_score: 0.4771 - val_balanced_accuracy: 0.6641 - val_specificity: 0.9918 - val_miss_rate: 0.6635 - val_fall_out: 0.0082 - val_mcc: 0.4963
Epoch 5/100
63/63 [==============================] - 5s 76ms/step - loss: 1.3007 - accuracy: 0.5427 - recall: 0.3441 - precision: 0.7540 - AUROC: 0.9042 - AUPRC: 0.5993 - f1_score: 0.4725 - balanced_accuracy: 0.6658 - specificity: 0.9875 - miss_rate: 0.6559 - fall_out: 0.0125 - mcc: 0.4767 - val_loss: 1.1936 - val_accuracy: 0.5879 - val_recall: 0.4076 - val_precision: 0.7760 - val_AUROC: 0.9179 - val_AUPRC: 0.6592 - val_f1_score: 0.5345 - val_balanced_accuracy: 0.6973 - val_specificity: 0.9869 - val_miss_rate: 0.5924 - val_fall_out: 0.0131 - val_mcc: 0.5306
Epoch 6/100
63/63 [==============================] - 5s 77ms/step - loss: 1.2145 - accuracy: 0.5723 - recall: 0.3902 - precision: 0.7695 - AUROC: 0.9166 - AUPRC: 0.6425 - f1_score: 0.5178 - balanced_accuracy: 0.6886 - specificity: 0.9870 - miss_rate: 0.6098 - fall_out: 0.0130 - mcc: 0.5158 - val_loss: 1.0941 - val_accuracy: 0.6319 - val_recall: 0.4146 - val_precision: 0.8231 - val_AUROC: 0.9333 - val_AUPRC: 0.7030 - val_f1_score: 0.5514 - val_balanced_accuracy: 0.7024 - val_specificity: 0.9901 - val_miss_rate: 0.5854 - val_fall_out: 0.0099 - val_mcc: 0.5551
Epoch 7/100
63/63 [==============================] - 5s 75ms/step - loss: 1.1301 - accuracy: 0.6068 - recall: 0.4282 - precision: 0.7838 - AUROC: 0.9281 - AUPRC: 0.6793 - f1_score: 0.5539 - balanced_accuracy: 0.7076 - specificity: 0.9869 - miss_rate: 0.5718 - fall_out: 0.0131 - mcc: 0.5480 - val_loss: 1.0497 - val_accuracy: 0.6575 - val_recall: 0.4662 - val_precision: 0.8174 - val_AUROC: 0.9364 - val_AUPRC: 0.7275 - val_f1_score: 0.5938 - val_balanced_accuracy: 0.7273 - val_specificity: 0.9884 - val_miss_rate: 0.5338 - val_fall_out: 0.0116 - val_mcc: 0.5881
Epoch 8/100
63/63 [==============================] - 5s 75ms/step - loss: 1.0466 - accuracy: 0.6368 - recall: 0.4736 - precision: 0.8058 - AUROC: 0.9382 - AUPRC: 0.7205 - f1_score: 0.5966 - balanced_accuracy: 0.7304 - specificity: 0.9873 - miss_rate: 0.5264 - fall_out: 0.0127 - mcc: 0.5879 - val_loss: 0.9849 - val_accuracy: 0.6765 - val_recall: 0.4667 - val_precision: 0.8574 - val_AUROC: 0.9465 - val_AUPRC: 0.7573 - val_f1_score: 0.6044 - val_balanced_accuracy: 0.7290 - val_specificity: 0.9914 - val_miss_rate: 0.5333 - val_fall_out: 0.0086 - val_mcc: 0.6057
Epoch 9/100
63/63 [==============================] - 5s 75ms/step - loss: 0.9696 - accuracy: 0.6690 - recall: 0.5149 - precision: 0.8170 - AUROC: 0.9465 - AUPRC: 0.7507 - f1_score: 0.6317 - balanced_accuracy: 0.7510 - specificity: 0.9872 - miss_rate: 0.4851 - fall_out: 0.0128 - mcc: 0.6198 - val_loss: 0.9728 - val_accuracy: 0.6810 - val_recall: 0.5428 - val_precision: 0.8181 - val_AUROC: 0.9449 - val_AUPRC: 0.7632 - val_f1_score: 0.6526 - val_balanced_accuracy: 0.7647 - val_specificity: 0.9866 - val_miss_rate: 0.4572 - val_fall_out: 0.0134 - val_mcc: 0.6381
Epoch 10/100
63/63 [==============================] - 5s 75ms/step - loss: 0.9015 - accuracy: 0.6955 - recall: 0.5610 - precision: 0.8252 - AUROC: 0.9535 - AUPRC: 0.7792 - f1_score: 0.6679 - balanced_accuracy: 0.7739 - specificity: 0.9868 - miss_rate: 0.4390 - fall_out: 0.0132 - mcc: 0.6529 - val_loss: 0.9348 - val_accuracy: 0.6940 - val_recall: 0.5588 - val_precision: 0.8297 - val_AUROC: 0.9495 - val_AUPRC: 0.7785 - val_f1_score: 0.6679 - val_balanced_accuracy: 0.7730 - val_specificity: 0.9873 - val_miss_rate: 0.4412 - val_fall_out: 0.0127 - val_mcc: 0.6537
Epoch 11/100
63/63 [==============================] - 5s 75ms/step - loss: 0.8462 - accuracy: 0.7113 - recall: 0.5865 - precision: 0.8364 - AUROC: 0.9589 - AUPRC: 0.8016 - f1_score: 0.6895 - balanced_accuracy: 0.7869 - specificity: 0.9873 - miss_rate: 0.4135 - fall_out: 0.0127 - mcc: 0.6741 - val_loss: 0.9563 - val_accuracy: 0.6790 - val_recall: 0.5508 - val_precision: 0.8106 - val_AUROC: 0.9466 - val_AUPRC: 0.7621 - val_f1_score: 0.6559 - val_balanced_accuracy: 0.7683 - val_specificity: 0.9857 - val_miss_rate: 0.4492 - val_fall_out: 0.0143 - val_mcc: 0.6396
Epoch 12/100
63/63 [==============================] - 5s 75ms/step - loss: 0.7382 - accuracy: 0.7482 - recall: 0.6346 - precision: 0.8522 - AUROC: 0.9686 - AUPRC: 0.8405 - f1_score: 0.7275 - balanced_accuracy: 0.8112 - specificity: 0.9878 - miss_rate: 0.3654 - fall_out: 0.0122 - mcc: 0.7112 - val_loss: 0.8947 - val_accuracy: 0.7236 - val_recall: 0.6340 - val_precision: 0.8079 - val_AUROC: 0.9524 - val_AUPRC: 0.7978 - val_f1_score: 0.7104 - val_balanced_accuracy: 0.8086 - val_specificity: 0.9833 - val_miss_rate: 0.3660 - val_fall_out: 0.0167 - val_mcc: 0.6886
Epoch 13/100
63/63 [==============================] - 5s 75ms/step - loss: 0.6783 - accuracy: 0.7645 - recall: 0.6746 - precision: 0.8604 - AUROC: 0.9727 - AUPRC: 0.8587 - f1_score: 0.7562 - balanced_accuracy: 0.8312 - specificity: 0.9878 - miss_rate: 0.3254 - fall_out: 0.0122 - mcc: 0.7393 - val_loss: 0.9172 - val_accuracy: 0.7116 - val_recall: 0.6224 - val_precision: 0.8071 - val_AUROC: 0.9511 - val_AUPRC: 0.7919 - val_f1_score: 0.7029 - val_balanced_accuracy: 0.8030 - val_specificity: 0.9835 - val_miss_rate: 0.3776 - val_fall_out: 0.0165 - val_mcc: 0.6814
Epoch 14/100
63/63 [==============================] - 5s 75ms/step - loss: 0.6651 - accuracy: 0.7693 - recall: 0.6837 - precision: 0.8625 - AUROC: 0.9739 - AUPRC: 0.8637 - f1_score: 0.7628 - balanced_accuracy: 0.8358 - specificity: 0.9879 - miss_rate: 0.3163 - fall_out: 0.0121 - mcc: 0.7458 - val_loss: 0.9296 - val_accuracy: 0.7086 - val_recall: 0.6269 - val_precision: 0.8062 - val_AUROC: 0.9507 - val_AUPRC: 0.7889 - val_f1_score: 0.7054 - val_balanced_accuracy: 0.8051 - val_specificity: 0.9833 - val_miss_rate: 0.3731 - val_fall_out: 0.0167 - val_mcc: 0.6836
250/250 [==============================] - 2s 9ms/step - loss: 0.3962 - accuracy: 0.8823 - recall: 0.7951 - precision: 0.9438 - AUROC: 0.9926 - AUPRC: 0.9529 - f1_score: 0.8631 - balanced_accuracy: 0.8949 - specificity: 0.9947 - miss_rate: 0.2049 - fall_out: 0.0053 - mcc: 0.8531
63/63 [==============================] - 1s 9ms/step - loss: 0.9296 - accuracy: 0.7086 - recall: 0.6269 - precision: 0.8062 - AUROC: 0.9507 - AUPRC: 0.7890 - f1_score: 0.7054 - balanced_accuracy: 0.8051 - specificity: 0.9833 - miss_rate: 0.3731 - fall_out: 0.0167 - mcc: 0.6836
6it [07:08, 70.57s/it]
-- HOLDOUT 7
Model: "CNN_S_3s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_112 (Conv2D) (None, 100, 130, 128) 1280
max_pooling2d_85 (MaxPoolin (None, 50, 65, 128) 0
g2D)
conv2d_113 (Conv2D) (None, 50, 65, 64) 73792
max_pooling2d_86 (MaxPoolin (None, 25, 33, 64) 0
g2D)
conv2d_114 (Conv2D) (None, 25, 33, 64) 36928
max_pooling2d_87 (MaxPoolin (None, 13, 17, 64) 0
g2D)
conv2d_115 (Conv2D) (None, 13, 17, 128) 131200
flatten_28 (Flatten) (None, 28288) 0
dense_84 (Dense) (None, 128) 3620992
dropout_58 (Dropout) (None, 128) 0
dense_85 (Dense) (None, 128) 16512
dropout_59 (Dropout) (None, 128) 0
dense_86 (Dense) (None, 10) 1290
=================================================================
Total params: 3,881,994
Trainable params: 3,881,994
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 6s 82ms/step - loss: 2.1435 - accuracy: 0.2095 - recall: 0.0385 - precision: 0.5927 - AUROC: 0.6671 - AUPRC: 0.2130 - f1_score: 0.0722 - balanced_accuracy: 0.5178 - specificity: 0.9971 - miss_rate: 0.9615 - fall_out: 0.0029 - mcc: 0.1327 - val_loss: 1.7366 - val_accuracy: 0.3465 - val_recall: 0.1292 - val_precision: 0.8012 - val_AUROC: 0.8328 - val_AUPRC: 0.4249 - val_f1_score: 0.2225 - val_balanced_accuracy: 0.5628 - val_specificity: 0.9964 - val_miss_rate: 0.8708 - val_fall_out: 0.0036 - val_mcc: 0.2992
Epoch 2/100
63/63 [==============================] - 5s 76ms/step - loss: 1.7749 - accuracy: 0.3518 - recall: 0.1480 - precision: 0.6813 - AUROC: 0.8071 - AUPRC: 0.3849 - f1_score: 0.2432 - balanced_accuracy: 0.5702 - specificity: 0.9923 - miss_rate: 0.8520 - fall_out: 0.0077 - mcc: 0.2888 - val_loss: 1.5760 - val_accuracy: 0.4111 - val_recall: 0.2283 - val_precision: 0.7250 - val_AUROC: 0.8516 - val_AUPRC: 0.4788 - val_f1_score: 0.3473 - val_balanced_accuracy: 0.6094 - val_specificity: 0.9904 - val_miss_rate: 0.7717 - val_fall_out: 0.0096 - val_mcc: 0.3757
Epoch 3/100
63/63 [==============================] - 5s 76ms/step - loss: 1.5808 - accuracy: 0.4188 - recall: 0.2110 - precision: 0.7068 - AUROC: 0.8529 - AUPRC: 0.4691 - f1_score: 0.3250 - balanced_accuracy: 0.6007 - specificity: 0.9903 - miss_rate: 0.7890 - fall_out: 0.0097 - mcc: 0.3549 - val_loss: 1.3400 - val_accuracy: 0.5093 - val_recall: 0.2954 - val_precision: 0.7794 - val_AUROC: 0.8972 - val_AUPRC: 0.5788 - val_f1_score: 0.4285 - val_balanced_accuracy: 0.6431 - val_specificity: 0.9907 - val_miss_rate: 0.7046 - val_fall_out: 0.0093 - val_mcc: 0.4495
Epoch 4/100
63/63 [==============================] - 5s 75ms/step - loss: 1.4554 - accuracy: 0.4749 - recall: 0.2664 - precision: 0.7434 - AUROC: 0.8785 - AUPRC: 0.5323 - f1_score: 0.3923 - balanced_accuracy: 0.6281 - specificity: 0.9898 - miss_rate: 0.7336 - fall_out: 0.0102 - mcc: 0.4135 - val_loss: 1.2298 - val_accuracy: 0.5764 - val_recall: 0.3365 - val_precision: 0.8096 - val_AUROC: 0.9160 - val_AUPRC: 0.6386 - val_f1_score: 0.4754 - val_balanced_accuracy: 0.6639 - val_specificity: 0.9912 - val_miss_rate: 0.6635 - val_fall_out: 0.0088 - val_mcc: 0.4926
Epoch 5/100
63/63 [==============================] - 5s 76ms/step - loss: 1.3641 - accuracy: 0.5056 - recall: 0.3055 - precision: 0.7466 - AUROC: 0.8950 - AUPRC: 0.5725 - f1_score: 0.4336 - balanced_accuracy: 0.6470 - specificity: 0.9885 - miss_rate: 0.6945 - fall_out: 0.0115 - mcc: 0.4452 - val_loss: 1.2031 - val_accuracy: 0.5959 - val_recall: 0.3831 - val_precision: 0.7919 - val_AUROC: 0.9169 - val_AUPRC: 0.6545 - val_f1_score: 0.5164 - val_balanced_accuracy: 0.6859 - val_specificity: 0.9888 - val_miss_rate: 0.6169 - val_fall_out: 0.0112 - val_mcc: 0.5200
Epoch 6/100
63/63 [==============================] - 5s 75ms/step - loss: 1.2391 - accuracy: 0.5681 - recall: 0.3676 - precision: 0.7716 - AUROC: 0.9134 - AUPRC: 0.6291 - f1_score: 0.4980 - balanced_accuracy: 0.6778 - specificity: 0.9879 - miss_rate: 0.6324 - fall_out: 0.0121 - mcc: 0.5007 - val_loss: 1.0785 - val_accuracy: 0.6375 - val_recall: 0.4311 - val_precision: 0.8367 - val_AUROC: 0.9347 - val_AUPRC: 0.7153 - val_f1_score: 0.5691 - val_balanced_accuracy: 0.7109 - val_specificity: 0.9907 - val_miss_rate: 0.5689 - val_fall_out: 0.0093 - val_mcc: 0.5724
Epoch 7/100
63/63 [==============================] - 5s 75ms/step - loss: 1.1575 - accuracy: 0.5931 - recall: 0.4203 - precision: 0.7876 - AUROC: 0.9243 - AUPRC: 0.6730 - f1_score: 0.5481 - balanced_accuracy: 0.7039 - specificity: 0.9874 - miss_rate: 0.5797 - fall_out: 0.0126 - mcc: 0.5442 - val_loss: 1.0400 - val_accuracy: 0.6435 - val_recall: 0.4477 - val_precision: 0.8418 - val_AUROC: 0.9390 - val_AUPRC: 0.7301 - val_f1_score: 0.5845 - val_balanced_accuracy: 0.7192 - val_specificity: 0.9907 - val_miss_rate: 0.5523 - val_fall_out: 0.0093 - val_mcc: 0.5860
Epoch 8/100
63/63 [==============================] - 5s 76ms/step - loss: 1.0733 - accuracy: 0.6273 - recall: 0.4620 - precision: 0.7921 - AUROC: 0.9352 - AUPRC: 0.7049 - f1_score: 0.5837 - balanced_accuracy: 0.7243 - specificity: 0.9865 - miss_rate: 0.5380 - fall_out: 0.0135 - mcc: 0.5742 - val_loss: 1.0739 - val_accuracy: 0.6520 - val_recall: 0.4306 - val_precision: 0.8183 - val_AUROC: 0.9372 - val_AUPRC: 0.7153 - val_f1_score: 0.5643 - val_balanced_accuracy: 0.7100 - val_specificity: 0.9894 - val_miss_rate: 0.5694 - val_fall_out: 0.0106 - val_mcc: 0.5643
Epoch 9/100
63/63 [==============================] - 5s 73ms/step - loss: 1.0065 - accuracy: 0.6519 - recall: 0.4907 - precision: 0.8135 - AUROC: 0.9425 - AUPRC: 0.7380 - f1_score: 0.6122 - balanced_accuracy: 0.7391 - specificity: 0.9875 - miss_rate: 0.5093 - fall_out: 0.0125 - mcc: 0.6026 - val_loss: 1.0407 - val_accuracy: 0.6600 - val_recall: 0.4692 - val_precision: 0.8092 - val_AUROC: 0.9384 - val_AUPRC: 0.7332 - val_f1_score: 0.5940 - val_balanced_accuracy: 0.7285 - val_specificity: 0.9877 - val_miss_rate: 0.5308 - val_fall_out: 0.0123 - val_mcc: 0.5865
250/250 [==============================] - 2s 9ms/step - loss: 0.7647 - accuracy: 0.7609 - recall: 0.5639 - precision: 0.8970 - AUROC: 0.9705 - AUPRC: 0.8458 - f1_score: 0.6925 - balanced_accuracy: 0.7783 - specificity: 0.9928 - miss_rate: 0.4361 - fall_out: 0.0072 - mcc: 0.6881
63/63 [==============================] - 1s 9ms/step - loss: 1.0407 - accuracy: 0.6600 - recall: 0.4692 - precision: 0.8092 - AUROC: 0.9384 - AUPRC: 0.7333 - f1_score: 0.5940 - balanced_accuracy: 0.7285 - specificity: 0.9877 - miss_rate: 0.5308 - fall_out: 0.0123 - mcc: 0.5865
7it [07:56, 62.91s/it]
-- HOLDOUT 8
Model: "CNN_S_3s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_116 (Conv2D) (None, 100, 130, 128) 1280
max_pooling2d_88 (MaxPoolin (None, 50, 65, 128) 0
g2D)
conv2d_117 (Conv2D) (None, 50, 65, 64) 73792
max_pooling2d_89 (MaxPoolin (None, 25, 33, 64) 0
g2D)
conv2d_118 (Conv2D) (None, 25, 33, 64) 36928
max_pooling2d_90 (MaxPoolin (None, 13, 17, 64) 0
g2D)
conv2d_119 (Conv2D) (None, 13, 17, 128) 131200
flatten_29 (Flatten) (None, 28288) 0
dense_87 (Dense) (None, 128) 3620992
dropout_60 (Dropout) (None, 128) 0
dense_88 (Dense) (None, 128) 16512
dropout_61 (Dropout) (None, 128) 0
dense_89 (Dense) (None, 10) 1290
=================================================================
Total params: 3,881,994
Trainable params: 3,881,994
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 6s 81ms/step - loss: 2.2067 - accuracy: 0.1893 - recall: 0.0237 - precision: 0.5608 - AUROC: 0.6263 - AUPRC: 0.1827 - f1_score: 0.0454 - balanced_accuracy: 0.5108 - specificity: 0.9979 - miss_rate: 0.9763 - fall_out: 0.0021 - mcc: 0.1000 - val_loss: 1.8757 - val_accuracy: 0.3360 - val_recall: 0.1242 - val_precision: 0.6375 - val_AUROC: 0.7985 - val_AUPRC: 0.3613 - val_f1_score: 0.2079 - val_balanced_accuracy: 0.5582 - val_specificity: 0.9922 - val_miss_rate: 0.8758 - val_fall_out: 0.0078 - val_mcc: 0.2525
Epoch 2/100
63/63 [==============================] - 5s 73ms/step - loss: 1.8601 - accuracy: 0.3258 - recall: 0.1301 - precision: 0.6738 - AUROC: 0.7774 - AUPRC: 0.3512 - f1_score: 0.2181 - balanced_accuracy: 0.5616 - specificity: 0.9930 - miss_rate: 0.8699 - fall_out: 0.0070 - mcc: 0.2684 - val_loss: 1.5160 - val_accuracy: 0.4572 - val_recall: 0.2308 - val_precision: 0.7853 - val_AUROC: 0.8647 - val_AUPRC: 0.5070 - val_f1_score: 0.3568 - val_balanced_accuracy: 0.6119 - val_specificity: 0.9930 - val_miss_rate: 0.7692 - val_fall_out: 0.0070 - val_mcc: 0.3976
Epoch 3/100
63/63 [==============================] - 5s 76ms/step - loss: 1.6329 - accuracy: 0.4108 - recall: 0.1951 - precision: 0.7098 - AUROC: 0.8423 - AUPRC: 0.4477 - f1_score: 0.3061 - balanced_accuracy: 0.5931 - specificity: 0.9911 - miss_rate: 0.8049 - fall_out: 0.0089 - mcc: 0.3418 - val_loss: 1.3157 - val_accuracy: 0.5368 - val_recall: 0.2894 - val_precision: 0.8281 - val_AUROC: 0.9061 - val_AUPRC: 0.6057 - val_f1_score: 0.4289 - val_balanced_accuracy: 0.6414 - val_specificity: 0.9933 - val_miss_rate: 0.7106 - val_fall_out: 0.0067 - val_mcc: 0.4619
Epoch 4/100
63/63 [==============================] - 5s 75ms/step - loss: 1.5002 - accuracy: 0.4629 - recall: 0.2474 - precision: 0.7226 - AUROC: 0.8702 - AUPRC: 0.5090 - f1_score: 0.3686 - balanced_accuracy: 0.6184 - specificity: 0.9895 - miss_rate: 0.7526 - fall_out: 0.0105 - mcc: 0.3907 - val_loss: 1.2523 - val_accuracy: 0.5653 - val_recall: 0.3170 - val_precision: 0.7834 - val_AUROC: 0.9127 - val_AUPRC: 0.6192 - val_f1_score: 0.4513 - val_balanced_accuracy: 0.6536 - val_specificity: 0.9903 - val_miss_rate: 0.6830 - val_fall_out: 0.0097 - val_mcc: 0.4678
Epoch 5/100
63/63 [==============================] - 5s 76ms/step - loss: 1.3745 - accuracy: 0.5121 - recall: 0.3084 - precision: 0.7458 - AUROC: 0.8923 - AUPRC: 0.5728 - f1_score: 0.4363 - balanced_accuracy: 0.6483 - specificity: 0.9883 - miss_rate: 0.6916 - fall_out: 0.0117 - mcc: 0.4471 - val_loss: 1.1713 - val_accuracy: 0.6019 - val_recall: 0.3635 - val_precision: 0.7978 - val_AUROC: 0.9244 - val_AUPRC: 0.6670 - val_f1_score: 0.4995 - val_balanced_accuracy: 0.6767 - val_specificity: 0.9898 - val_miss_rate: 0.6365 - val_fall_out: 0.0102 - val_mcc: 0.5082
Epoch 6/100
63/63 [==============================] - 5s 76ms/step - loss: 1.2968 - accuracy: 0.5422 - recall: 0.3499 - precision: 0.7483 - AUROC: 0.9054 - AUPRC: 0.6078 - f1_score: 0.4769 - balanced_accuracy: 0.6684 - specificity: 0.9869 - miss_rate: 0.6501 - fall_out: 0.0131 - mcc: 0.4786 - val_loss: 1.1561 - val_accuracy: 0.6044 - val_recall: 0.3620 - val_precision: 0.8387 - val_AUROC: 0.9270 - val_AUPRC: 0.6803 - val_f1_score: 0.5058 - val_balanced_accuracy: 0.6772 - val_specificity: 0.9923 - val_miss_rate: 0.6380 - val_fall_out: 0.0077 - val_mcc: 0.5230
Epoch 7/100
63/63 [==============================] - 5s 75ms/step - loss: 1.1719 - accuracy: 0.5886 - recall: 0.4084 - precision: 0.7764 - AUROC: 0.9227 - AUPRC: 0.6603 - f1_score: 0.5353 - balanced_accuracy: 0.6977 - specificity: 0.9869 - miss_rate: 0.5916 - fall_out: 0.0131 - mcc: 0.5313 - val_loss: 1.0519 - val_accuracy: 0.6600 - val_recall: 0.4166 - val_precision: 0.8533 - val_AUROC: 0.9410 - val_AUPRC: 0.7296 - val_f1_score: 0.5599 - val_balanced_accuracy: 0.7043 - val_specificity: 0.9920 - val_miss_rate: 0.5834 - val_fall_out: 0.0080 - val_mcc: 0.5689
Epoch 8/100
63/63 [==============================] - 5s 75ms/step - loss: 1.0832 - accuracy: 0.6271 - recall: 0.4638 - precision: 0.8052 - AUROC: 0.9332 - AUPRC: 0.7042 - f1_score: 0.5886 - balanced_accuracy: 0.7257 - specificity: 0.9875 - miss_rate: 0.5362 - fall_out: 0.0125 - mcc: 0.5811 - val_loss: 1.0024 - val_accuracy: 0.6580 - val_recall: 0.4817 - val_precision: 0.8358 - val_AUROC: 0.9433 - val_AUPRC: 0.7425 - val_f1_score: 0.6112 - val_balanced_accuracy: 0.7356 - val_specificity: 0.9895 - val_miss_rate: 0.5183 - val_fall_out: 0.0105 - val_mcc: 0.6066
Epoch 9/100
63/63 [==============================] - 5s 75ms/step - loss: 1.0034 - accuracy: 0.6589 - recall: 0.5041 - precision: 0.8121 - AUROC: 0.9427 - AUPRC: 0.7397 - f1_score: 0.6221 - balanced_accuracy: 0.7456 - specificity: 0.9870 - miss_rate: 0.4959 - fall_out: 0.0130 - mcc: 0.6107 - val_loss: 0.9736 - val_accuracy: 0.6815 - val_recall: 0.4842 - val_precision: 0.8603 - val_AUROC: 0.9475 - val_AUPRC: 0.7591 - val_f1_score: 0.6197 - val_balanced_accuracy: 0.7377 - val_specificity: 0.9913 - val_miss_rate: 0.5158 - val_fall_out: 0.0087 - val_mcc: 0.6189
Epoch 10/100
63/63 [==============================] - 5s 73ms/step - loss: 0.9677 - accuracy: 0.6695 - recall: 0.5228 - precision: 0.8159 - AUROC: 0.9471 - AUPRC: 0.7530 - f1_score: 0.6373 - balanced_accuracy: 0.7548 - specificity: 0.9869 - miss_rate: 0.4772 - fall_out: 0.0131 - mcc: 0.6244 - val_loss: 0.9298 - val_accuracy: 0.6850 - val_recall: 0.5608 - val_precision: 0.8040 - val_AUROC: 0.9502 - val_AUPRC: 0.7675 - val_f1_score: 0.6608 - val_balanced_accuracy: 0.7728 - val_specificity: 0.9848 - val_miss_rate: 0.4392 - val_fall_out: 0.0152 - val_mcc: 0.6426
Epoch 11/100
63/63 [==============================] - 5s 73ms/step - loss: 0.8664 - accuracy: 0.7053 - recall: 0.5701 - precision: 0.8337 - AUROC: 0.9575 - AUPRC: 0.7936 - f1_score: 0.6772 - balanced_accuracy: 0.7788 - specificity: 0.9874 - miss_rate: 0.4299 - fall_out: 0.0126 - mcc: 0.6626 - val_loss: 0.9088 - val_accuracy: 0.6895 - val_recall: 0.5934 - val_precision: 0.8034 - val_AUROC: 0.9519 - val_AUPRC: 0.7800 - val_f1_score: 0.6826 - val_balanced_accuracy: 0.7886 - val_specificity: 0.9839 - val_miss_rate: 0.4066 - val_fall_out: 0.0161 - val_mcc: 0.6621
Epoch 12/100
63/63 [==============================] - 5s 75ms/step - loss: 0.8137 - accuracy: 0.7212 - recall: 0.6027 - precision: 0.8379 - AUROC: 0.9618 - AUPRC: 0.8121 - f1_score: 0.7011 - balanced_accuracy: 0.7949 - specificity: 0.9870 - miss_rate: 0.3973 - fall_out: 0.0130 - mcc: 0.6848 - val_loss: 0.9245 - val_accuracy: 0.7016 - val_recall: 0.5899 - val_precision: 0.8003 - val_AUROC: 0.9502 - val_AUPRC: 0.7779 - val_f1_score: 0.6792 - val_balanced_accuracy: 0.7868 - val_specificity: 0.9836 - val_miss_rate: 0.4101 - val_fall_out: 0.0164 - val_mcc: 0.6585
Epoch 13/100
63/63 [==============================] - 5s 76ms/step - loss: 0.7228 - accuracy: 0.7561 - recall: 0.6567 - precision: 0.8648 - AUROC: 0.9698 - AUPRC: 0.8474 - f1_score: 0.7465 - balanced_accuracy: 0.8226 - specificity: 0.9886 - miss_rate: 0.3433 - fall_out: 0.0114 - mcc: 0.7308 - val_loss: 0.9390 - val_accuracy: 0.6895 - val_recall: 0.5989 - val_precision: 0.7858 - val_AUROC: 0.9489 - val_AUPRC: 0.7748 - val_f1_score: 0.6797 - val_balanced_accuracy: 0.7904 - val_specificity: 0.9819 - val_miss_rate: 0.4011 - val_fall_out: 0.0181 - val_mcc: 0.6566
250/250 [==============================] - 2s 9ms/step - loss: 0.4892 - accuracy: 0.8471 - recall: 0.7541 - precision: 0.9239 - AUROC: 0.9874 - AUPRC: 0.9273 - f1_score: 0.8304 - balanced_accuracy: 0.8736 - specificity: 0.9931 - miss_rate: 0.2459 - fall_out: 0.0069 - mcc: 0.8188
63/63 [==============================] - 1s 9ms/step - loss: 0.9390 - accuracy: 0.6895 - recall: 0.5989 - precision: 0.7858 - AUROC: 0.9489 - AUPRC: 0.7747 - f1_score: 0.6797 - balanced_accuracy: 0.7904 - specificity: 0.9819 - miss_rate: 0.4011 - fall_out: 0.0181 - mcc: 0.6566
8it [09:02, 63.94s/it]
-- HOLDOUT 9
Model: "CNN_S_3s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_120 (Conv2D) (None, 100, 130, 128) 1280
max_pooling2d_91 (MaxPoolin (None, 50, 65, 128) 0
g2D)
conv2d_121 (Conv2D) (None, 50, 65, 64) 73792
max_pooling2d_92 (MaxPoolin (None, 25, 33, 64) 0
g2D)
conv2d_122 (Conv2D) (None, 25, 33, 64) 36928
max_pooling2d_93 (MaxPoolin (None, 13, 17, 64) 0
g2D)
conv2d_123 (Conv2D) (None, 13, 17, 128) 131200
flatten_30 (Flatten) (None, 28288) 0
dense_90 (Dense) (None, 128) 3620992
dropout_62 (Dropout) (None, 128) 0
dense_91 (Dense) (None, 128) 16512
dropout_63 (Dropout) (None, 128) 0
dense_92 (Dense) (None, 10) 1290
=================================================================
Total params: 3,881,994
Trainable params: 3,881,994
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 6s 82ms/step - loss: 2.1695 - accuracy: 0.1973 - recall: 0.0261 - precision: 0.5061 - AUROC: 0.6616 - AUPRC: 0.1963 - f1_score: 0.0496 - balanced_accuracy: 0.5116 - specificity: 0.9972 - miss_rate: 0.9739 - fall_out: 0.0028 - mcc: 0.0974 - val_loss: 1.7225 - val_accuracy: 0.3891 - val_recall: 0.1077 - val_precision: 0.7934 - val_AUROC: 0.8366 - val_AUPRC: 0.4407 - val_f1_score: 0.1896 - val_balanced_accuracy: 0.5523 - val_specificity: 0.9969 - val_miss_rate: 0.8923 - val_fall_out: 0.0031 - val_mcc: 0.2711
Epoch 2/100
63/63 [==============================] - 5s 76ms/step - loss: 1.8076 - accuracy: 0.3515 - recall: 0.1333 - precision: 0.6572 - AUROC: 0.7995 - AUPRC: 0.3692 - f1_score: 0.2216 - balanced_accuracy: 0.5628 - specificity: 0.9923 - miss_rate: 0.8667 - fall_out: 0.0077 - mcc: 0.2672 - val_loss: 1.5162 - val_accuracy: 0.4852 - val_recall: 0.1988 - val_precision: 0.8519 - val_AUROC: 0.8814 - val_AUPRC: 0.5339 - val_f1_score: 0.3224 - val_balanced_accuracy: 0.5975 - val_specificity: 0.9962 - val_miss_rate: 0.8012 - val_fall_out: 0.0038 - val_mcc: 0.3874
Epoch 3/100
63/63 [==============================] - 5s 74ms/step - loss: 1.5833 - accuracy: 0.4246 - recall: 0.2097 - precision: 0.6978 - AUROC: 0.8539 - AUPRC: 0.4646 - f1_score: 0.3225 - balanced_accuracy: 0.5998 - specificity: 0.9899 - miss_rate: 0.7903 - fall_out: 0.0101 - mcc: 0.3507 - val_loss: 1.2907 - val_accuracy: 0.5518 - val_recall: 0.2744 - val_precision: 0.8278 - val_AUROC: 0.9102 - val_AUPRC: 0.6165 - val_f1_score: 0.4122 - val_balanced_accuracy: 0.6340 - val_specificity: 0.9937 - val_miss_rate: 0.7256 - val_fall_out: 0.0063 - val_mcc: 0.4492
Epoch 4/100
63/63 [==============================] - 5s 75ms/step - loss: 1.4594 - accuracy: 0.4738 - recall: 0.2579 - precision: 0.7222 - AUROC: 0.8782 - AUPRC: 0.5260 - f1_score: 0.3801 - balanced_accuracy: 0.6234 - specificity: 0.9890 - miss_rate: 0.7421 - fall_out: 0.0110 - mcc: 0.3991 - val_loss: 1.2226 - val_accuracy: 0.6124 - val_recall: 0.3085 - val_precision: 0.8485 - val_AUROC: 0.9208 - val_AUPRC: 0.6579 - val_f1_score: 0.4524 - val_balanced_accuracy: 0.6512 - val_specificity: 0.9939 - val_miss_rate: 0.6915 - val_fall_out: 0.0061 - val_mcc: 0.4846
Epoch 5/100
63/63 [==============================] - 5s 74ms/step - loss: 1.3323 - accuracy: 0.5309 - recall: 0.3275 - precision: 0.7597 - AUROC: 0.8993 - AUPRC: 0.5892 - f1_score: 0.4577 - balanced_accuracy: 0.6580 - specificity: 0.9885 - miss_rate: 0.6725 - fall_out: 0.0115 - mcc: 0.4668 - val_loss: 1.1791 - val_accuracy: 0.6004 - val_recall: 0.3746 - val_precision: 0.8104 - val_AUROC: 0.9231 - val_AUPRC: 0.6631 - val_f1_score: 0.5123 - val_balanced_accuracy: 0.6824 - val_specificity: 0.9903 - val_miss_rate: 0.6254 - val_fall_out: 0.0097 - val_mcc: 0.5213
Epoch 6/100
63/63 [==============================] - 5s 75ms/step - loss: 1.2539 - accuracy: 0.5629 - recall: 0.3686 - precision: 0.7640 - AUROC: 0.9119 - AUPRC: 0.6278 - f1_score: 0.4973 - balanced_accuracy: 0.6780 - specificity: 0.9873 - miss_rate: 0.6314 - fall_out: 0.0127 - mcc: 0.4983 - val_loss: 1.0199 - val_accuracy: 0.6700 - val_recall: 0.4266 - val_precision: 0.8802 - val_AUROC: 0.9459 - val_AUPRC: 0.7523 - val_f1_score: 0.5747 - val_balanced_accuracy: 0.7101 - val_specificity: 0.9935 - val_miss_rate: 0.5734 - val_fall_out: 0.0065 - val_mcc: 0.5870
Epoch 7/100
63/63 [==============================] - 5s 75ms/step - loss: 1.1528 - accuracy: 0.6008 - recall: 0.4264 - precision: 0.7863 - AUROC: 0.9248 - AUPRC: 0.6734 - f1_score: 0.5529 - balanced_accuracy: 0.7067 - specificity: 0.9871 - miss_rate: 0.5736 - fall_out: 0.0129 - mcc: 0.5478 - val_loss: 1.0394 - val_accuracy: 0.6425 - val_recall: 0.4572 - val_precision: 0.8533 - val_AUROC: 0.9398 - val_AUPRC: 0.7282 - val_f1_score: 0.5954 - val_balanced_accuracy: 0.7242 - val_specificity: 0.9913 - val_miss_rate: 0.5428 - val_fall_out: 0.0087 - val_mcc: 0.5974
Epoch 8/100
63/63 [==============================] - 5s 75ms/step - loss: 1.0831 - accuracy: 0.6216 - recall: 0.4624 - precision: 0.7874 - AUROC: 0.9340 - AUPRC: 0.7046 - f1_score: 0.5827 - balanced_accuracy: 0.7243 - specificity: 0.9861 - miss_rate: 0.5376 - fall_out: 0.0139 - mcc: 0.5723 - val_loss: 0.9794 - val_accuracy: 0.6730 - val_recall: 0.5088 - val_precision: 0.8253 - val_AUROC: 0.9460 - val_AUPRC: 0.7511 - val_f1_score: 0.6295 - val_balanced_accuracy: 0.7484 - val_specificity: 0.9880 - val_miss_rate: 0.4912 - val_fall_out: 0.0120 - val_mcc: 0.6197
Epoch 9/100
63/63 [==============================] - 5s 75ms/step - loss: 1.0075 - accuracy: 0.6573 - recall: 0.5004 - precision: 0.8092 - AUROC: 0.9425 - AUPRC: 0.7389 - f1_score: 0.6184 - balanced_accuracy: 0.7436 - specificity: 0.9869 - miss_rate: 0.4996 - fall_out: 0.0131 - mcc: 0.6069 - val_loss: 0.9391 - val_accuracy: 0.6900 - val_recall: 0.5173 - val_precision: 0.8474 - val_AUROC: 0.9511 - val_AUPRC: 0.7756 - val_f1_score: 0.6424 - val_balanced_accuracy: 0.7535 - val_specificity: 0.9897 - val_miss_rate: 0.4827 - val_fall_out: 0.0103 - val_mcc: 0.6352
Epoch 10/100
63/63 [==============================] - 5s 75ms/step - loss: 0.9062 - accuracy: 0.6901 - recall: 0.5485 - precision: 0.8298 - AUROC: 0.9534 - AUPRC: 0.7781 - f1_score: 0.6604 - balanced_accuracy: 0.7680 - specificity: 0.9875 - miss_rate: 0.4515 - fall_out: 0.0125 - mcc: 0.6472 - val_loss: 0.8710 - val_accuracy: 0.7111 - val_recall: 0.5874 - val_precision: 0.8385 - val_AUROC: 0.9556 - val_AUPRC: 0.7995 - val_f1_score: 0.6908 - val_balanced_accuracy: 0.7874 - val_specificity: 0.9874 - val_miss_rate: 0.4126 - val_fall_out: 0.0126 - val_mcc: 0.6756
Epoch 11/100
63/63 [==============================] - 5s 75ms/step - loss: 0.8974 - accuracy: 0.6949 - recall: 0.5612 - precision: 0.8257 - AUROC: 0.9542 - AUPRC: 0.7842 - f1_score: 0.6683 - balanced_accuracy: 0.7740 - specificity: 0.9868 - miss_rate: 0.4388 - fall_out: 0.0132 - mcc: 0.6533 - val_loss: 0.9505 - val_accuracy: 0.6815 - val_recall: 0.5679 - val_precision: 0.7936 - val_AUROC: 0.9474 - val_AUPRC: 0.7629 - val_f1_score: 0.6620 - val_balanced_accuracy: 0.7757 - val_specificity: 0.9836 - val_miss_rate: 0.4321 - val_fall_out: 0.0164 - val_mcc: 0.6418
Epoch 12/100
63/63 [==============================] - 5s 75ms/step - loss: 0.7872 - accuracy: 0.7282 - recall: 0.6154 - precision: 0.8490 - AUROC: 0.9642 - AUPRC: 0.8232 - f1_score: 0.7135 - balanced_accuracy: 0.8016 - specificity: 0.9878 - miss_rate: 0.3846 - fall_out: 0.0122 - mcc: 0.6979 - val_loss: 0.8445 - val_accuracy: 0.7226 - val_recall: 0.6159 - val_precision: 0.8283 - val_AUROC: 0.9582 - val_AUPRC: 0.8082 - val_f1_score: 0.7065 - val_balanced_accuracy: 0.8009 - val_specificity: 0.9858 - val_miss_rate: 0.3841 - val_fall_out: 0.0142 - val_mcc: 0.6881
Epoch 13/100
63/63 [==============================] - 5s 75ms/step - loss: 0.7455 - accuracy: 0.7449 - recall: 0.6435 - precision: 0.8589 - AUROC: 0.9678 - AUPRC: 0.8420 - f1_score: 0.7358 - balanced_accuracy: 0.8159 - specificity: 0.9883 - miss_rate: 0.3565 - fall_out: 0.0117 - mcc: 0.7199 - val_loss: 0.8094 - val_accuracy: 0.7306 - val_recall: 0.6585 - val_precision: 0.8208 - val_AUROC: 0.9613 - val_AUPRC: 0.8251 - val_f1_score: 0.7308 - val_balanced_accuracy: 0.8213 - val_specificity: 0.9840 - val_miss_rate: 0.3415 - val_fall_out: 0.0160 - val_mcc: 0.7096
Epoch 14/100
63/63 [==============================] - 5s 74ms/step - loss: 0.6787 - accuracy: 0.7621 - recall: 0.6742 - precision: 0.8596 - AUROC: 0.9728 - AUPRC: 0.8596 - f1_score: 0.7557 - balanced_accuracy: 0.8310 - specificity: 0.9878 - miss_rate: 0.3258 - fall_out: 0.0122 - mcc: 0.7387 - val_loss: 0.9332 - val_accuracy: 0.7061 - val_recall: 0.6144 - val_precision: 0.7825 - val_AUROC: 0.9494 - val_AUPRC: 0.7846 - val_f1_score: 0.6884 - val_balanced_accuracy: 0.7977 - val_specificity: 0.9810 - val_miss_rate: 0.3856 - val_fall_out: 0.0190 - val_mcc: 0.6641
Epoch 15/100
63/63 [==============================] - 5s 73ms/step - loss: 0.6405 - accuracy: 0.7796 - recall: 0.6931 - precision: 0.8727 - AUROC: 0.9758 - AUPRC: 0.8747 - f1_score: 0.7726 - balanced_accuracy: 0.8410 - specificity: 0.9888 - miss_rate: 0.3069 - fall_out: 0.0112 - mcc: 0.7566 - val_loss: 0.8262 - val_accuracy: 0.7336 - val_recall: 0.6650 - val_precision: 0.8127 - val_AUROC: 0.9594 - val_AUPRC: 0.8218 - val_f1_score: 0.7315 - val_balanced_accuracy: 0.8240 - val_specificity: 0.9830 - val_miss_rate: 0.3350 - val_fall_out: 0.0170 - val_mcc: 0.7092
250/250 [==============================] - 2s 10ms/step - loss: 0.3222 - accuracy: 0.9067 - recall: 0.8423 - precision: 0.9553 - AUROC: 0.9951 - AUPRC: 0.9688 - f1_score: 0.8952 - balanced_accuracy: 0.9190 - specificity: 0.9956 - miss_rate: 0.1577 - fall_out: 0.0044 - mcc: 0.8865
63/63 [==============================] - 1s 9ms/step - loss: 0.8262 - accuracy: 0.7336 - recall: 0.6655 - precision: 0.8123 - AUROC: 0.9594 - AUPRC: 0.8218 - f1_score: 0.7316 - balanced_accuracy: 0.8242 - specificity: 0.9829 - miss_rate: 0.3345 - fall_out: 0.0171 - mcc: 0.7093
9it [10:17, 67.53s/it]
-- HOLDOUT 10
Model: "CNN_S_3s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_124 (Conv2D) (None, 100, 130, 128) 1280
max_pooling2d_94 (MaxPoolin (None, 50, 65, 128) 0
g2D)
conv2d_125 (Conv2D) (None, 50, 65, 64) 73792
max_pooling2d_95 (MaxPoolin (None, 25, 33, 64) 0
g2D)
conv2d_126 (Conv2D) (None, 25, 33, 64) 36928
max_pooling2d_96 (MaxPoolin (None, 13, 17, 64) 0
g2D)
conv2d_127 (Conv2D) (None, 13, 17, 128) 131200
flatten_31 (Flatten) (None, 28288) 0
dense_93 (Dense) (None, 128) 3620992
dropout_64 (Dropout) (None, 128) 0
dense_94 (Dense) (None, 128) 16512
dropout_65 (Dropout) (None, 128) 0
dense_95 (Dense) (None, 10) 1290
=================================================================
Total params: 3,881,994
Trainable params: 3,881,994
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 6s 84ms/step - loss: 2.2015 - accuracy: 0.1921 - recall: 0.0242 - precision: 0.5562 - AUROC: 0.6295 - AUPRC: 0.1829 - f1_score: 0.0463 - balanced_accuracy: 0.5110 - specificity: 0.9979 - miss_rate: 0.9758 - fall_out: 0.0021 - mcc: 0.1005 - val_loss: 1.8597 - val_accuracy: 0.3300 - val_recall: 0.0696 - val_precision: 0.8742 - val_AUROC: 0.7980 - val_AUPRC: 0.3685 - val_f1_score: 0.1289 - val_balanced_accuracy: 0.5342 - val_specificity: 0.9989 - val_miss_rate: 0.9304 - val_fall_out: 0.0011 - val_mcc: 0.2312
Epoch 2/100
63/63 [==============================] - 5s 73ms/step - loss: 1.8533 - accuracy: 0.3156 - recall: 0.1096 - precision: 0.6322 - AUROC: 0.7856 - AUPRC: 0.3361 - f1_score: 0.1868 - balanced_accuracy: 0.5513 - specificity: 0.9929 - miss_rate: 0.8904 - fall_out: 0.0071 - mcc: 0.2356 - val_loss: 1.5457 - val_accuracy: 0.4437 - val_recall: 0.2048 - val_precision: 0.8051 - val_AUROC: 0.8680 - val_AUPRC: 0.4954 - val_f1_score: 0.3265 - val_balanced_accuracy: 0.5996 - val_specificity: 0.9945 - val_miss_rate: 0.7952 - val_fall_out: 0.0055 - val_mcc: 0.3797
Epoch 3/100
63/63 [==============================] - 5s 73ms/step - loss: 1.6374 - accuracy: 0.3950 - recall: 0.1857 - precision: 0.6927 - AUROC: 0.8415 - AUPRC: 0.4385 - f1_score: 0.2929 - balanced_accuracy: 0.5883 - specificity: 0.9908 - miss_rate: 0.8143 - fall_out: 0.0092 - mcc: 0.3279 - val_loss: 1.5243 - val_accuracy: 0.4522 - val_recall: 0.2384 - val_precision: 0.7094 - val_AUROC: 0.8662 - val_AUPRC: 0.5031 - val_f1_score: 0.3568 - val_balanced_accuracy: 0.6138 - val_specificity: 0.9892 - val_miss_rate: 0.7616 - val_fall_out: 0.0108 - val_mcc: 0.3788
Epoch 4/100
63/63 [==============================] - 5s 73ms/step - loss: 1.5157 - accuracy: 0.4520 - recall: 0.2375 - precision: 0.7185 - AUROC: 0.8669 - AUPRC: 0.5006 - f1_score: 0.3570 - balanced_accuracy: 0.6136 - specificity: 0.9897 - miss_rate: 0.7625 - fall_out: 0.0103 - mcc: 0.3811 - val_loss: 1.3267 - val_accuracy: 0.5328 - val_recall: 0.2734 - val_precision: 0.8077 - val_AUROC: 0.9033 - val_AUPRC: 0.6000 - val_f1_score: 0.4085 - val_balanced_accuracy: 0.6331 - val_specificity: 0.9928 - val_miss_rate: 0.7266 - val_fall_out: 0.0072 - val_mcc: 0.4416
Epoch 5/100
63/63 [==============================] - 5s 73ms/step - loss: 1.4026 - accuracy: 0.4962 - recall: 0.2925 - precision: 0.7470 - AUROC: 0.8882 - AUPRC: 0.5555 - f1_score: 0.4203 - balanced_accuracy: 0.6407 - specificity: 0.9890 - miss_rate: 0.7075 - fall_out: 0.0110 - mcc: 0.4353 - val_loss: 1.2210 - val_accuracy: 0.5814 - val_recall: 0.3210 - val_precision: 0.8336 - val_AUROC: 0.9202 - val_AUPRC: 0.6470 - val_f1_score: 0.4635 - val_balanced_accuracy: 0.6569 - val_specificity: 0.9929 - val_miss_rate: 0.6790 - val_fall_out: 0.0071 - val_mcc: 0.4893
Epoch 6/100
63/63 [==============================] - 5s 74ms/step - loss: 1.2949 - accuracy: 0.5462 - recall: 0.3414 - precision: 0.7653 - AUROC: 0.9049 - AUPRC: 0.6102 - f1_score: 0.4722 - balanced_accuracy: 0.6649 - specificity: 0.9884 - miss_rate: 0.6586 - fall_out: 0.0116 - mcc: 0.4792 - val_loss: 1.1157 - val_accuracy: 0.6104 - val_recall: 0.3851 - val_precision: 0.8535 - val_AUROC: 0.9304 - val_AUPRC: 0.6916 - val_f1_score: 0.5307 - val_balanced_accuracy: 0.6889 - val_specificity: 0.9927 - val_miss_rate: 0.6149 - val_fall_out: 0.0073 - val_mcc: 0.5460
Epoch 7/100
63/63 [==============================] - 5s 75ms/step - loss: 1.1899 - accuracy: 0.5844 - recall: 0.3993 - precision: 0.7806 - AUROC: 0.9197 - AUPRC: 0.6574 - f1_score: 0.5283 - balanced_accuracy: 0.6934 - specificity: 0.9875 - miss_rate: 0.6007 - fall_out: 0.0125 - mcc: 0.5268 - val_loss: 1.0330 - val_accuracy: 0.6525 - val_recall: 0.4462 - val_precision: 0.8374 - val_AUROC: 0.9415 - val_AUPRC: 0.7330 - val_f1_score: 0.5822 - val_balanced_accuracy: 0.7183 - val_specificity: 0.9904 - val_miss_rate: 0.5538 - val_fall_out: 0.0096 - val_mcc: 0.5831
Epoch 8/100
63/63 [==============================] - 5s 74ms/step - loss: 1.0933 - accuracy: 0.6226 - recall: 0.4547 - precision: 0.7943 - AUROC: 0.9321 - AUPRC: 0.7012 - f1_score: 0.5783 - balanced_accuracy: 0.7208 - specificity: 0.9869 - miss_rate: 0.5453 - fall_out: 0.0131 - mcc: 0.5703 - val_loss: 1.0821 - val_accuracy: 0.6345 - val_recall: 0.4537 - val_precision: 0.7996 - val_AUROC: 0.9334 - val_AUPRC: 0.7146 - val_f1_score: 0.5789 - val_balanced_accuracy: 0.7205 - val_specificity: 0.9874 - val_miss_rate: 0.5463 - val_fall_out: 0.0126 - val_mcc: 0.5720
Epoch 9/100
63/63 [==============================] - 5s 74ms/step - loss: 1.0438 - accuracy: 0.6358 - recall: 0.4826 - precision: 0.8142 - AUROC: 0.9383 - AUPRC: 0.7243 - f1_score: 0.6060 - balanced_accuracy: 0.7352 - specificity: 0.9878 - miss_rate: 0.5174 - fall_out: 0.0122 - mcc: 0.5976 - val_loss: 0.9478 - val_accuracy: 0.6775 - val_recall: 0.4957 - val_precision: 0.8512 - val_AUROC: 0.9499 - val_AUPRC: 0.7665 - val_f1_score: 0.6266 - val_balanced_accuracy: 0.7431 - val_specificity: 0.9904 - val_miss_rate: 0.5043 - val_fall_out: 0.0096 - val_mcc: 0.6227
Epoch 10/100
63/63 [==============================] - 5s 73ms/step - loss: 0.9752 - accuracy: 0.6653 - recall: 0.5193 - precision: 0.8187 - AUROC: 0.9457 - AUPRC: 0.7518 - f1_score: 0.6355 - balanced_accuracy: 0.7533 - specificity: 0.9872 - miss_rate: 0.4807 - fall_out: 0.0128 - mcc: 0.6235 - val_loss: 0.9250 - val_accuracy: 0.6825 - val_recall: 0.5273 - val_precision: 0.8227 - val_AUROC: 0.9516 - val_AUPRC: 0.7722 - val_f1_score: 0.6427 - val_balanced_accuracy: 0.7573 - val_specificity: 0.9874 - val_miss_rate: 0.4727 - val_fall_out: 0.0126 - val_mcc: 0.6304
Epoch 11/100
63/63 [==============================] - 5s 73ms/step - loss: 0.8765 - accuracy: 0.7007 - recall: 0.5701 - precision: 0.8420 - AUROC: 0.9559 - AUPRC: 0.7936 - f1_score: 0.6799 - balanced_accuracy: 0.7791 - specificity: 0.9881 - miss_rate: 0.4299 - fall_out: 0.0119 - mcc: 0.6666 - val_loss: 0.9784 - val_accuracy: 0.6655 - val_recall: 0.5463 - val_precision: 0.7883 - val_AUROC: 0.9447 - val_AUPRC: 0.7490 - val_f1_score: 0.6454 - val_balanced_accuracy: 0.7650 - val_specificity: 0.9837 - val_miss_rate: 0.4537 - val_fall_out: 0.0163 - val_mcc: 0.6261
Epoch 12/100
63/63 [==============================] - 5s 74ms/step - loss: 0.8236 - accuracy: 0.7219 - recall: 0.6016 - precision: 0.8463 - AUROC: 0.9611 - AUPRC: 0.8134 - f1_score: 0.7033 - balanced_accuracy: 0.7947 - specificity: 0.9879 - miss_rate: 0.3984 - fall_out: 0.0121 - mcc: 0.6882 - val_loss: 0.8788 - val_accuracy: 0.7066 - val_recall: 0.6094 - val_precision: 0.8070 - val_AUROC: 0.9547 - val_AUPRC: 0.7929 - val_f1_score: 0.6944 - val_balanced_accuracy: 0.7966 - val_specificity: 0.9838 - val_miss_rate: 0.3906 - val_fall_out: 0.0162 - val_mcc: 0.6736
Epoch 13/100
63/63 [==============================] - 5s 74ms/step - loss: 0.7698 - accuracy: 0.7321 - recall: 0.6254 - precision: 0.8534 - AUROC: 0.9659 - AUPRC: 0.8288 - f1_score: 0.7218 - balanced_accuracy: 0.8067 - specificity: 0.9881 - miss_rate: 0.3746 - fall_out: 0.0119 - mcc: 0.7062 - val_loss: 0.8847 - val_accuracy: 0.7026 - val_recall: 0.5949 - val_precision: 0.8239 - val_AUROC: 0.9547 - val_AUPRC: 0.7894 - val_f1_score: 0.6909 - val_balanced_accuracy: 0.7904 - val_specificity: 0.9859 - val_miss_rate: 0.4051 - val_fall_out: 0.0141 - val_mcc: 0.6731
Epoch 14/100
63/63 [==============================] - 5s 74ms/step - loss: 0.7079 - accuracy: 0.7564 - recall: 0.6551 - precision: 0.8649 - AUROC: 0.9708 - AUPRC: 0.8526 - f1_score: 0.7455 - balanced_accuracy: 0.8218 - specificity: 0.9886 - miss_rate: 0.3449 - fall_out: 0.0114 - mcc: 0.7299 - val_loss: 0.9515 - val_accuracy: 0.6835 - val_recall: 0.5979 - val_precision: 0.7814 - val_AUROC: 0.9483 - val_AUPRC: 0.7718 - val_f1_score: 0.6774 - val_balanced_accuracy: 0.7897 - val_specificity: 0.9814 - val_miss_rate: 0.4021 - val_fall_out: 0.0186 - val_mcc: 0.6538
250/250 [==============================] - 2s 10ms/step - loss: 0.4638 - accuracy: 0.8521 - recall: 0.7768 - precision: 0.9214 - AUROC: 0.9885 - AUPRC: 0.9342 - f1_score: 0.8429 - balanced_accuracy: 0.8847 - specificity: 0.9926 - miss_rate: 0.2232 - fall_out: 0.0074 - mcc: 0.8308
63/63 [==============================] - 1s 9ms/step - loss: 0.9515 - accuracy: 0.6830 - recall: 0.5979 - precision: 0.7814 - AUROC: 0.9483 - AUPRC: 0.7718 - f1_score: 0.6774 - balanced_accuracy: 0.7897 - specificity: 0.9814 - miss_rate: 0.4021 - fall_out: 0.0186 - mcc: 0.6538
10it [11:27, 68.74s/it]
CNN_metrics_estimate = model_metrics_holdout_estimate(CNN_S_3s_metrics, number_of_splits)
print(f"CNN Metrics - {number_of_splits}-holdouts estimate:")
print(f"Accuracy : train - {CNN_metrics_estimate['accuracy_train']} -- test - {CNN_metrics_estimate['accuracy_test']}")
print(f"AUROC : train - {CNN_metrics_estimate['AUROC_train']} -- test - {CNN_metrics_estimate['AUROC_test']}")
print(f"AUPRC : train - {CNN_metrics_estimate['AUPRC_train']} -- test - {CNN_metrics_estimate['AUPRC_test']}")
print("-"*80)
print("CNN - Train history:")
plot_train_history(CNN_S_3s_history)
print("-"*100)
CNN Metrics - 10-holdouts estimate: Accuracy : train - 0.8699649333953857 -- test - 0.704757136106491 AUROC : train - 0.9898907244205475 -- test - 0.951000201702118 AUPRC : train - 0.9411249101161957 -- test - 0.7865305840969086 -------------------------------------------------------------------------------- CNN - Train history:
----------------------------------------------------------------------------------------------------
data['mel_spectrogram_3s'][sample].shape
(128, 130)
print("---- 3s window Mel Spectrogram - Fixed CNN ----")
input_data = [np.expand_dims(x, axis=-1) for x in data['mel_spectrogram_3s']]
data_labels = data['labels_3s']
CNN_MelS_3s_metrics = []
CNN_MelS_3s_history = []
# Generate holdouts
for holdout_number, (train_indices, test_indices) in enumerate(tqdm(holdouts_generator.split(input_data, data_labels))):
print(f"-- HOLDOUT {holdout_number+1}")
# Train/Test data
x_train, x_test = np.array([input_data[x] for x in train_indices]), np.array([input_data[x] for x in test_indices])
y_train, y_test = data_labels.iloc[train_indices], data_labels.iloc[test_indices]
# One-hot encoding
y_train = one_hot_encoding(y_train, 10)
y_test = one_hot_encoding(y_test, 10)
# Build CNN
CNN = build_fixed_CNN_melS(x_train.shape[1:])
print("- Training model:\n")
CNN_holdout_metrics, CNN_holdout_history = train_model(
CNN,
np.array(x_train),
np.array(x_test),
y_train.values,
y_test.values,
epochs,
batch_size
)
CNN_MelS_3s_metrics.append(CNN_holdout_metrics)
CNN_MelS_3s_history.append(CNN_holdout_history)
---- 3s window Mel Spectrogram - Fixed CNN ----
0it [00:00, ?it/s]
-- HOLDOUT 1
Model: "CNN_MelS_3s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_12 (Conv2D) (None, 128, 130, 64) 640
max_pooling2d_9 (MaxPooling (None, 64, 65, 64) 0
2D)
conv2d_13 (Conv2D) (None, 64, 65, 64) 36928
max_pooling2d_10 (MaxPoolin (None, 32, 33, 64) 0
g2D)
conv2d_14 (Conv2D) (None, 32, 33, 128) 73856
max_pooling2d_11 (MaxPoolin (None, 16, 17, 128) 0
g2D)
conv2d_15 (Conv2D) (None, 16, 17, 128) 262272
flatten_3 (Flatten) (None, 34816) 0
dense_9 (Dense) (None, 128) 4456576
dropout_6 (Dropout) (None, 128) 0
dense_10 (Dense) (None, 128) 16512
dropout_7 (Dropout) (None, 128) 0
dense_11 (Dense) (None, 10) 1290
=================================================================
Total params: 4,848,074
Trainable params: 4,848,074
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 7s 90ms/step - loss: 3.3610 - accuracy: 0.1784 - recall: 0.0108 - precision: 0.1604 - AUROC: 0.5826 - AUPRC: 0.1320 - f1_score: 0.0202 - balanced_accuracy: 0.5023 - specificity: 0.9937 - miss_rate: 0.9892 - fall_out: 0.0063 - mcc: 0.0166 - val_loss: 2.1255 - val_accuracy: 0.2734 - val_recall: 0.0065 - val_precision: 0.8125 - val_AUROC: 0.7391 - val_AUPRC: 0.2366 - val_f1_score: 0.0129 - val_balanced_accuracy: 0.5032 - val_specificity: 0.9998 - val_miss_rate: 0.9935 - val_fall_out: 1.6692e-04 - val_mcc: 0.0673
Epoch 2/100
63/63 [==============================] - 4s 67ms/step - loss: 2.1467 - accuracy: 0.2343 - recall: 0.0243 - precision: 0.4924 - AUROC: 0.6856 - AUPRC: 0.2015 - f1_score: 0.0463 - balanced_accuracy: 0.5108 - specificity: 0.9972 - miss_rate: 0.9757 - fall_out: 0.0028 - mcc: 0.0921 - val_loss: 2.0721 - val_accuracy: 0.2444 - val_recall: 0.0145 - val_precision: 0.6304 - val_AUROC: 0.7453 - val_AUPRC: 0.2662 - val_f1_score: 0.0284 - val_balanced_accuracy: 0.5068 - val_specificity: 0.9991 - val_miss_rate: 0.9855 - val_fall_out: 9.4586e-04 - val_mcc: 0.0850
Epoch 3/100
63/63 [==============================] - 4s 66ms/step - loss: 1.9823 - accuracy: 0.2683 - recall: 0.0612 - precision: 0.5421 - AUROC: 0.7477 - AUPRC: 0.2665 - f1_score: 0.1101 - balanced_accuracy: 0.5277 - specificity: 0.9943 - miss_rate: 0.9388 - fall_out: 0.0057 - mcc: 0.1575 - val_loss: 1.7287 - val_accuracy: 0.4116 - val_recall: 0.1067 - val_precision: 0.6339 - val_AUROC: 0.8491 - val_AUPRC: 0.4073 - val_f1_score: 0.1826 - val_balanced_accuracy: 0.5499 - val_specificity: 0.9932 - val_miss_rate: 0.8933 - val_fall_out: 0.0068 - val_mcc: 0.2328
Epoch 4/100
63/63 [==============================] - 4s 66ms/step - loss: 1.7771 - accuracy: 0.3542 - recall: 0.1192 - precision: 0.5957 - AUROC: 0.8127 - AUPRC: 0.3586 - f1_score: 0.1987 - balanced_accuracy: 0.5551 - specificity: 0.9910 - miss_rate: 0.8808 - fall_out: 0.0090 - mcc: 0.2362 - val_loss: 1.5760 - val_accuracy: 0.4692 - val_recall: 0.1713 - val_precision: 0.7339 - val_AUROC: 0.8652 - val_AUPRC: 0.4826 - val_f1_score: 0.2777 - val_balanced_accuracy: 0.5822 - val_specificity: 0.9931 - val_miss_rate: 0.8287 - val_fall_out: 0.0069 - val_mcc: 0.3266
Epoch 5/100
63/63 [==============================] - 4s 66ms/step - loss: 1.6046 - accuracy: 0.4386 - recall: 0.1885 - precision: 0.6645 - AUROC: 0.8524 - AUPRC: 0.4491 - f1_score: 0.2937 - balanced_accuracy: 0.5890 - specificity: 0.9894 - miss_rate: 0.8115 - fall_out: 0.0106 - mcc: 0.3215 - val_loss: 1.4594 - val_accuracy: 0.5613 - val_recall: 0.2609 - val_precision: 0.7012 - val_AUROC: 0.8862 - val_AUPRC: 0.5448 - val_f1_score: 0.3803 - val_balanced_accuracy: 0.6243 - val_specificity: 0.9876 - val_miss_rate: 0.7391 - val_fall_out: 0.0124 - val_mcc: 0.3940
Epoch 6/100
63/63 [==============================] - 4s 66ms/step - loss: 1.4560 - accuracy: 0.4861 - recall: 0.2633 - precision: 0.6944 - AUROC: 0.8800 - AUPRC: 0.5200 - f1_score: 0.3818 - balanced_accuracy: 0.6252 - specificity: 0.9871 - miss_rate: 0.7367 - fall_out: 0.0129 - mcc: 0.3933 - val_loss: 1.2202 - val_accuracy: 0.6079 - val_recall: 0.3821 - val_precision: 0.8023 - val_AUROC: 0.9158 - val_AUPRC: 0.6556 - val_f1_score: 0.5176 - val_balanced_accuracy: 0.6858 - val_specificity: 0.9895 - val_miss_rate: 0.6179 - val_fall_out: 0.0105 - val_mcc: 0.5235
Epoch 7/100
63/63 [==============================] - 4s 65ms/step - loss: 1.3261 - accuracy: 0.5377 - recall: 0.3317 - precision: 0.7335 - AUROC: 0.9006 - AUPRC: 0.5921 - f1_score: 0.4568 - balanced_accuracy: 0.6591 - specificity: 0.9866 - miss_rate: 0.6683 - fall_out: 0.0134 - mcc: 0.4595 - val_loss: 1.2261 - val_accuracy: 0.6054 - val_recall: 0.4131 - val_precision: 0.7925 - val_AUROC: 0.9151 - val_AUPRC: 0.6591 - val_f1_score: 0.5431 - val_balanced_accuracy: 0.7006 - val_specificity: 0.9880 - val_miss_rate: 0.5869 - val_fall_out: 0.0120 - val_mcc: 0.5413
Epoch 8/100
63/63 [==============================] - 4s 66ms/step - loss: 1.2287 - accuracy: 0.5848 - recall: 0.3918 - precision: 0.7655 - AUROC: 0.9142 - AUPRC: 0.6422 - f1_score: 0.5183 - balanced_accuracy: 0.6892 - specificity: 0.9867 - miss_rate: 0.6082 - fall_out: 0.0133 - mcc: 0.5152 - val_loss: 1.0430 - val_accuracy: 0.6595 - val_recall: 0.4677 - val_precision: 0.8244 - val_AUROC: 0.9391 - val_AUPRC: 0.7361 - val_f1_score: 0.5968 - val_balanced_accuracy: 0.7283 - val_specificity: 0.9889 - val_miss_rate: 0.5323 - val_fall_out: 0.0111 - val_mcc: 0.5922
Epoch 9/100
63/63 [==============================] - 4s 65ms/step - loss: 1.1515 - accuracy: 0.6123 - recall: 0.4316 - precision: 0.7796 - AUROC: 0.9253 - AUPRC: 0.6775 - f1_score: 0.5556 - balanced_accuracy: 0.7090 - specificity: 0.9864 - miss_rate: 0.5684 - fall_out: 0.0136 - mcc: 0.5484 - val_loss: 0.9802 - val_accuracy: 0.6935 - val_recall: 0.5193 - val_precision: 0.8486 - val_AUROC: 0.9448 - val_AUPRC: 0.7549 - val_f1_score: 0.6443 - val_balanced_accuracy: 0.7545 - val_specificity: 0.9897 - val_miss_rate: 0.4807 - val_fall_out: 0.0103 - val_mcc: 0.6371
Epoch 10/100
63/63 [==============================] - 4s 66ms/step - loss: 1.0278 - accuracy: 0.6569 - recall: 0.4985 - precision: 0.8076 - AUROC: 0.9400 - AUPRC: 0.7322 - f1_score: 0.6165 - balanced_accuracy: 0.7427 - specificity: 0.9868 - miss_rate: 0.5015 - fall_out: 0.0132 - mcc: 0.6050 - val_loss: 1.0046 - val_accuracy: 0.6785 - val_recall: 0.5223 - val_precision: 0.8278 - val_AUROC: 0.9429 - val_AUPRC: 0.7458 - val_f1_score: 0.6405 - val_balanced_accuracy: 0.7551 - val_specificity: 0.9879 - val_miss_rate: 0.4777 - val_fall_out: 0.0121 - val_mcc: 0.6295
Epoch 11/100
63/63 [==============================] - 4s 66ms/step - loss: 0.9634 - accuracy: 0.6765 - recall: 0.5272 - precision: 0.8197 - AUROC: 0.9469 - AUPRC: 0.7603 - f1_score: 0.6417 - balanced_accuracy: 0.7571 - specificity: 0.9871 - miss_rate: 0.4728 - fall_out: 0.0129 - mcc: 0.6289 - val_loss: 0.9663 - val_accuracy: 0.6965 - val_recall: 0.5764 - val_precision: 0.8032 - val_AUROC: 0.9463 - val_AUPRC: 0.7672 - val_f1_score: 0.6711 - val_balanced_accuracy: 0.7803 - val_specificity: 0.9843 - val_miss_rate: 0.4236 - val_fall_out: 0.0157 - val_mcc: 0.6517
Epoch 12/100
63/63 [==============================] - 4s 66ms/step - loss: 0.9212 - accuracy: 0.7002 - recall: 0.5630 - precision: 0.8278 - AUROC: 0.9513 - AUPRC: 0.7806 - f1_score: 0.6702 - balanced_accuracy: 0.7750 - specificity: 0.9870 - miss_rate: 0.4370 - fall_out: 0.0130 - mcc: 0.6554 - val_loss: 0.8875 - val_accuracy: 0.7231 - val_recall: 0.6049 - val_precision: 0.8268 - val_AUROC: 0.9533 - val_AUPRC: 0.7968 - val_f1_score: 0.6987 - val_balanced_accuracy: 0.7954 - val_specificity: 0.9859 - val_miss_rate: 0.3951 - val_fall_out: 0.0141 - val_mcc: 0.6807
Epoch 13/100
63/63 [==============================] - 4s 66ms/step - loss: 0.8213 - accuracy: 0.7276 - recall: 0.6126 - precision: 0.8494 - AUROC: 0.9610 - AUPRC: 0.8158 - f1_score: 0.7118 - balanced_accuracy: 0.8003 - specificity: 0.9879 - miss_rate: 0.3874 - fall_out: 0.0121 - mcc: 0.6964 - val_loss: 0.9575 - val_accuracy: 0.6860 - val_recall: 0.6019 - val_precision: 0.7913 - val_AUROC: 0.9461 - val_AUPRC: 0.7703 - val_f1_score: 0.6837 - val_balanced_accuracy: 0.7921 - val_specificity: 0.9824 - val_miss_rate: 0.3981 - val_fall_out: 0.0176 - val_mcc: 0.6612
Epoch 14/100
63/63 [==============================] - 4s 66ms/step - loss: 0.8270 - accuracy: 0.7291 - recall: 0.6122 - precision: 0.8442 - AUROC: 0.9599 - AUPRC: 0.8130 - f1_score: 0.7097 - balanced_accuracy: 0.7998 - specificity: 0.9874 - miss_rate: 0.3878 - fall_out: 0.0126 - mcc: 0.6937 - val_loss: 0.8705 - val_accuracy: 0.7301 - val_recall: 0.6184 - val_precision: 0.8553 - val_AUROC: 0.9554 - val_AUPRC: 0.8098 - val_f1_score: 0.7178 - val_balanced_accuracy: 0.8034 - val_specificity: 0.9884 - val_miss_rate: 0.3816 - val_fall_out: 0.0116 - val_mcc: 0.7029
Epoch 15/100
63/63 [==============================] - 4s 66ms/step - loss: 0.7121 - accuracy: 0.7677 - recall: 0.6673 - precision: 0.8649 - AUROC: 0.9699 - AUPRC: 0.8546 - f1_score: 0.7534 - balanced_accuracy: 0.8279 - specificity: 0.9884 - miss_rate: 0.3327 - fall_out: 0.0116 - mcc: 0.7373 - val_loss: 0.8690 - val_accuracy: 0.7376 - val_recall: 0.6675 - val_precision: 0.8158 - val_AUROC: 0.9558 - val_AUPRC: 0.8142 - val_f1_score: 0.7342 - val_balanced_accuracy: 0.8254 - val_specificity: 0.9833 - val_miss_rate: 0.3325 - val_fall_out: 0.0167 - val_mcc: 0.7123
Epoch 16/100
63/63 [==============================] - 4s 66ms/step - loss: 0.7003 - accuracy: 0.7727 - recall: 0.6820 - precision: 0.8670 - AUROC: 0.9710 - AUPRC: 0.8598 - f1_score: 0.7635 - balanced_accuracy: 0.8352 - specificity: 0.9884 - miss_rate: 0.3180 - fall_out: 0.0116 - mcc: 0.7471 - val_loss: 0.9392 - val_accuracy: 0.7221 - val_recall: 0.6400 - val_precision: 0.7973 - val_AUROC: 0.9498 - val_AUPRC: 0.7914 - val_f1_score: 0.7100 - val_balanced_accuracy: 0.8109 - val_specificity: 0.9819 - val_miss_rate: 0.3600 - val_fall_out: 0.0181 - val_mcc: 0.6866
Epoch 17/100
63/63 [==============================] - 4s 66ms/step - loss: 0.6790 - accuracy: 0.7793 - recall: 0.6885 - precision: 0.8662 - AUROC: 0.9722 - AUPRC: 0.8650 - f1_score: 0.7672 - balanced_accuracy: 0.8383 - specificity: 0.9882 - miss_rate: 0.3115 - fall_out: 0.0118 - mcc: 0.7505 - val_loss: 0.9386 - val_accuracy: 0.7206 - val_recall: 0.6264 - val_precision: 0.8166 - val_AUROC: 0.9504 - val_AUPRC: 0.7980 - val_f1_score: 0.7090 - val_balanced_accuracy: 0.8054 - val_specificity: 0.9844 - val_miss_rate: 0.3736 - val_fall_out: 0.0156 - val_mcc: 0.6885
250/250 [==============================] - 2s 9ms/step - loss: 0.4519 - accuracy: 0.8600 - recall: 0.7717 - precision: 0.9291 - AUROC: 0.9889 - AUPRC: 0.9366 - f1_score: 0.8431 - balanced_accuracy: 0.8826 - specificity: 0.9935 - miss_rate: 0.2283 - fall_out: 0.0065 - mcc: 0.8318
63/63 [==============================] - 1s 10ms/step - loss: 0.9386 - accuracy: 0.7206 - recall: 0.6264 - precision: 0.8166 - AUROC: 0.9504 - AUPRC: 0.7980 - f1_score: 0.7090 - balanced_accuracy: 0.8054 - specificity: 0.9844 - miss_rate: 0.3736 - fall_out: 0.0156 - mcc: 0.6885
1it [01:17, 77.65s/it]
-- HOLDOUT 2
Model: "CNN_MelS_3s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_16 (Conv2D) (None, 128, 130, 64) 640
max_pooling2d_12 (MaxPoolin (None, 64, 65, 64) 0
g2D)
conv2d_17 (Conv2D) (None, 64, 65, 64) 36928
max_pooling2d_13 (MaxPoolin (None, 32, 33, 64) 0
g2D)
conv2d_18 (Conv2D) (None, 32, 33, 128) 73856
max_pooling2d_14 (MaxPoolin (None, 16, 17, 128) 0
g2D)
conv2d_19 (Conv2D) (None, 16, 17, 128) 262272
flatten_4 (Flatten) (None, 34816) 0
dense_12 (Dense) (None, 128) 4456576
dropout_8 (Dropout) (None, 128) 0
dense_13 (Dense) (None, 128) 16512
dropout_9 (Dropout) (None, 128) 0
dense_14 (Dense) (None, 10) 1290
=================================================================
Total params: 4,848,074
Trainable params: 4,848,074
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 6s 74ms/step - loss: 2.9945 - accuracy: 0.1616 - recall: 0.0149 - precision: 0.2262 - AUROC: 0.5992 - AUPRC: 0.1455 - f1_score: 0.0280 - balanced_accuracy: 0.5046 - specificity: 0.9943 - miss_rate: 0.9851 - fall_out: 0.0057 - mcc: 0.0343 - val_loss: 2.0310 - val_accuracy: 0.2313 - val_recall: 0.0441 - val_precision: 0.7586 - val_AUROC: 0.7323 - val_AUPRC: 0.2774 - val_f1_score: 0.0833 - val_balanced_accuracy: 0.5213 - val_specificity: 0.9984 - val_miss_rate: 0.9559 - val_fall_out: 0.0016 - val_mcc: 0.1678
Epoch 2/100
63/63 [==============================] - 4s 66ms/step - loss: 2.0492 - accuracy: 0.2315 - recall: 0.0458 - precision: 0.5048 - AUROC: 0.7215 - AUPRC: 0.2403 - f1_score: 0.0841 - balanced_accuracy: 0.5204 - specificity: 0.9950 - miss_rate: 0.9542 - fall_out: 0.0050 - mcc: 0.1292 - val_loss: 1.9128 - val_accuracy: 0.2679 - val_recall: 0.0991 - val_precision: 0.5964 - val_AUROC: 0.7621 - val_AUPRC: 0.2947 - val_f1_score: 0.1700 - val_balanced_accuracy: 0.5458 - val_specificity: 0.9925 - val_miss_rate: 0.9009 - val_fall_out: 0.0075 - val_mcc: 0.2151
Epoch 3/100
63/63 [==============================] - 4s 66ms/step - loss: 1.9317 - accuracy: 0.2900 - recall: 0.0730 - precision: 0.5655 - AUROC: 0.7678 - AUPRC: 0.2913 - f1_score: 0.1293 - balanced_accuracy: 0.5334 - specificity: 0.9938 - miss_rate: 0.9270 - fall_out: 0.0062 - mcc: 0.1775 - val_loss: 1.7658 - val_accuracy: 0.3826 - val_recall: 0.0826 - val_precision: 0.7746 - val_AUROC: 0.8349 - val_AUPRC: 0.3987 - val_f1_score: 0.1493 - val_balanced_accuracy: 0.5400 - val_specificity: 0.9973 - val_miss_rate: 0.9174 - val_fall_out: 0.0027 - val_mcc: 0.2335
Epoch 4/100
63/63 [==============================] - 4s 66ms/step - loss: 1.7974 - accuracy: 0.3330 - recall: 0.1102 - precision: 0.6032 - AUROC: 0.8074 - AUPRC: 0.3470 - f1_score: 0.1864 - balanced_accuracy: 0.5511 - specificity: 0.9919 - miss_rate: 0.8898 - fall_out: 0.0081 - mcc: 0.2288 - val_loss: 1.5872 - val_accuracy: 0.4922 - val_recall: 0.1327 - val_precision: 0.8055 - val_AUROC: 0.8738 - val_AUPRC: 0.4822 - val_f1_score: 0.2279 - val_balanced_accuracy: 0.5646 - val_specificity: 0.9964 - val_miss_rate: 0.8673 - val_fall_out: 0.0036 - val_mcc: 0.3044
Epoch 5/100
63/63 [==============================] - 4s 64ms/step - loss: 1.6650 - accuracy: 0.3942 - recall: 0.1546 - precision: 0.6302 - AUROC: 0.8410 - AUPRC: 0.4144 - f1_score: 0.2482 - balanced_accuracy: 0.5722 - specificity: 0.9899 - miss_rate: 0.8454 - fall_out: 0.0101 - mcc: 0.2802 - val_loss: 1.4609 - val_accuracy: 0.4967 - val_recall: 0.2228 - val_precision: 0.7712 - val_AUROC: 0.8768 - val_AUPRC: 0.5277 - val_f1_score: 0.3458 - val_balanced_accuracy: 0.6077 - val_specificity: 0.9927 - val_miss_rate: 0.7772 - val_fall_out: 0.0073 - val_mcc: 0.3859
Epoch 6/100
63/63 [==============================] - 4s 67ms/step - loss: 1.5282 - accuracy: 0.4583 - recall: 0.2288 - precision: 0.6861 - AUROC: 0.8665 - AUPRC: 0.4861 - f1_score: 0.3432 - balanced_accuracy: 0.6086 - specificity: 0.9884 - miss_rate: 0.7712 - fall_out: 0.0116 - mcc: 0.3629 - val_loss: 1.3616 - val_accuracy: 0.5348 - val_recall: 0.2934 - val_precision: 0.7887 - val_AUROC: 0.8948 - val_AUPRC: 0.5725 - val_f1_score: 0.4277 - val_balanced_accuracy: 0.6424 - val_specificity: 0.9913 - val_miss_rate: 0.7066 - val_fall_out: 0.0087 - val_mcc: 0.4513
Epoch 7/100
63/63 [==============================] - 4s 64ms/step - loss: 1.3965 - accuracy: 0.5109 - recall: 0.2774 - precision: 0.7157 - AUROC: 0.8912 - AUPRC: 0.5495 - f1_score: 0.3999 - balanced_accuracy: 0.6326 - specificity: 0.9878 - miss_rate: 0.7226 - fall_out: 0.0122 - mcc: 0.4121 - val_loss: 1.1783 - val_accuracy: 0.6079 - val_recall: 0.3650 - val_precision: 0.8457 - val_AUROC: 0.9241 - val_AUPRC: 0.6717 - val_f1_score: 0.5100 - val_balanced_accuracy: 0.6788 - val_specificity: 0.9926 - val_miss_rate: 0.6350 - val_fall_out: 0.0074 - val_mcc: 0.5280
Epoch 8/100
63/63 [==============================] - 4s 68ms/step - loss: 1.2688 - accuracy: 0.5612 - recall: 0.3543 - precision: 0.7381 - AUROC: 0.9102 - AUPRC: 0.6121 - f1_score: 0.4788 - balanced_accuracy: 0.6702 - specificity: 0.9860 - miss_rate: 0.6457 - fall_out: 0.0140 - mcc: 0.4776 - val_loss: 1.1062 - val_accuracy: 0.6415 - val_recall: 0.4291 - val_precision: 0.8240 - val_AUROC: 0.9321 - val_AUPRC: 0.6981 - val_f1_score: 0.5644 - val_balanced_accuracy: 0.7095 - val_specificity: 0.9898 - val_miss_rate: 0.5709 - val_fall_out: 0.0102 - val_mcc: 0.5657
Epoch 9/100
63/63 [==============================] - 4s 68ms/step - loss: 1.1874 - accuracy: 0.5909 - recall: 0.4036 - precision: 0.7697 - AUROC: 0.9214 - AUPRC: 0.6561 - f1_score: 0.5295 - balanced_accuracy: 0.6951 - specificity: 0.9866 - miss_rate: 0.5964 - fall_out: 0.0134 - mcc: 0.5251 - val_loss: 1.1070 - val_accuracy: 0.6405 - val_recall: 0.4712 - val_precision: 0.7822 - val_AUROC: 0.9317 - val_AUPRC: 0.6982 - val_f1_score: 0.5881 - val_balanced_accuracy: 0.7283 - val_specificity: 0.9854 - val_miss_rate: 0.5288 - val_fall_out: 0.0146 - val_mcc: 0.5757
Epoch 10/100
63/63 [==============================] - 4s 66ms/step - loss: 1.0837 - accuracy: 0.6365 - recall: 0.4656 - precision: 0.7837 - AUROC: 0.9341 - AUPRC: 0.7048 - f1_score: 0.5841 - balanced_accuracy: 0.7256 - specificity: 0.9857 - miss_rate: 0.5344 - fall_out: 0.0143 - mcc: 0.5727 - val_loss: 1.0550 - val_accuracy: 0.6274 - val_recall: 0.5083 - val_precision: 0.7420 - val_AUROC: 0.9374 - val_AUPRC: 0.7011 - val_f1_score: 0.6033 - val_balanced_accuracy: 0.7443 - val_specificity: 0.9804 - val_miss_rate: 0.4917 - val_fall_out: 0.0196 - val_mcc: 0.5803
Epoch 11/100
63/63 [==============================] - 4s 66ms/step - loss: 1.0330 - accuracy: 0.6537 - recall: 0.4966 - precision: 0.8031 - AUROC: 0.9397 - AUPRC: 0.7265 - f1_score: 0.6137 - balanced_accuracy: 0.7415 - specificity: 0.9865 - miss_rate: 0.5034 - fall_out: 0.0135 - mcc: 0.6017 - val_loss: 0.9233 - val_accuracy: 0.6985 - val_recall: 0.5543 - val_precision: 0.8438 - val_AUROC: 0.9514 - val_AUPRC: 0.7844 - val_f1_score: 0.6691 - val_balanced_accuracy: 0.7715 - val_specificity: 0.9886 - val_miss_rate: 0.4457 - val_fall_out: 0.0114 - val_mcc: 0.6574
Epoch 12/100
63/63 [==============================] - 4s 66ms/step - loss: 0.9381 - accuracy: 0.6879 - recall: 0.5383 - precision: 0.8131 - AUROC: 0.9502 - AUPRC: 0.7634 - f1_score: 0.6478 - balanced_accuracy: 0.7623 - specificity: 0.9863 - miss_rate: 0.4617 - fall_out: 0.0137 - mcc: 0.6329 - val_loss: 0.9290 - val_accuracy: 0.6900 - val_recall: 0.5658 - val_precision: 0.8182 - val_AUROC: 0.9512 - val_AUPRC: 0.7802 - val_f1_score: 0.6690 - val_balanced_accuracy: 0.7759 - val_specificity: 0.9860 - val_miss_rate: 0.4342 - val_fall_out: 0.0140 - val_mcc: 0.6526
Epoch 13/100
63/63 [==============================] - 4s 66ms/step - loss: 0.8579 - accuracy: 0.7149 - recall: 0.5909 - precision: 0.8339 - AUROC: 0.9577 - AUPRC: 0.7986 - f1_score: 0.6917 - balanced_accuracy: 0.7889 - specificity: 0.9869 - miss_rate: 0.4091 - fall_out: 0.0131 - mcc: 0.6756 - val_loss: 0.8633 - val_accuracy: 0.7361 - val_recall: 0.6314 - val_precision: 0.8345 - val_AUROC: 0.9567 - val_AUPRC: 0.8126 - val_f1_score: 0.7189 - val_balanced_accuracy: 0.8088 - val_specificity: 0.9861 - val_miss_rate: 0.3686 - val_fall_out: 0.0139 - val_mcc: 0.7005
Epoch 14/100
63/63 [==============================] - 4s 66ms/step - loss: 0.7949 - accuracy: 0.7409 - recall: 0.6279 - precision: 0.8508 - AUROC: 0.9643 - AUPRC: 0.8270 - f1_score: 0.7225 - balanced_accuracy: 0.8078 - specificity: 0.9878 - miss_rate: 0.3721 - fall_out: 0.0122 - mcc: 0.7064 - val_loss: 0.8666 - val_accuracy: 0.7346 - val_recall: 0.6385 - val_precision: 0.8274 - val_AUROC: 0.9566 - val_AUPRC: 0.8121 - val_f1_score: 0.7207 - val_balanced_accuracy: 0.8118 - val_specificity: 0.9852 - val_miss_rate: 0.3615 - val_fall_out: 0.0148 - val_mcc: 0.7011
Epoch 15/100
63/63 [==============================] - 4s 66ms/step - loss: 0.7727 - accuracy: 0.7514 - recall: 0.6435 - precision: 0.8469 - AUROC: 0.9647 - AUPRC: 0.8322 - f1_score: 0.7313 - balanced_accuracy: 0.8153 - specificity: 0.9871 - miss_rate: 0.3565 - fall_out: 0.0129 - mcc: 0.7139 - val_loss: 0.7904 - val_accuracy: 0.7391 - val_recall: 0.6555 - val_precision: 0.8348 - val_AUROC: 0.9629 - val_AUPRC: 0.8283 - val_f1_score: 0.7344 - val_balanced_accuracy: 0.8205 - val_specificity: 0.9856 - val_miss_rate: 0.3445 - val_fall_out: 0.0144 - val_mcc: 0.7150
Epoch 16/100
63/63 [==============================] - 4s 66ms/step - loss: 0.6946 - accuracy: 0.7778 - recall: 0.6835 - precision: 0.8670 - AUROC: 0.9710 - AUPRC: 0.8590 - f1_score: 0.7644 - balanced_accuracy: 0.8359 - specificity: 0.9884 - miss_rate: 0.3165 - fall_out: 0.0116 - mcc: 0.7479 - val_loss: 0.8075 - val_accuracy: 0.7401 - val_recall: 0.6665 - val_precision: 0.8403 - val_AUROC: 0.9603 - val_AUPRC: 0.8252 - val_f1_score: 0.7434 - val_balanced_accuracy: 0.8262 - val_specificity: 0.9859 - val_miss_rate: 0.3335 - val_fall_out: 0.0141 - val_mcc: 0.7243
Epoch 17/100
63/63 [==============================] - 4s 66ms/step - loss: 0.6535 - accuracy: 0.7926 - recall: 0.7030 - precision: 0.8704 - AUROC: 0.9745 - AUPRC: 0.8737 - f1_score: 0.7778 - balanced_accuracy: 0.8457 - specificity: 0.9884 - miss_rate: 0.2970 - fall_out: 0.0116 - mcc: 0.7612 - val_loss: 0.8211 - val_accuracy: 0.7421 - val_recall: 0.6800 - val_precision: 0.8196 - val_AUROC: 0.9610 - val_AUPRC: 0.8330 - val_f1_score: 0.7433 - val_balanced_accuracy: 0.8317 - val_specificity: 0.9834 - val_miss_rate: 0.3200 - val_fall_out: 0.0166 - val_mcc: 0.7215
250/250 [==============================] - 2s 8ms/step - loss: 0.3651 - accuracy: 0.8871 - recall: 0.8308 - precision: 0.9365 - AUROC: 0.9919 - AUPRC: 0.9560 - f1_score: 0.8805 - balanced_accuracy: 0.9123 - specificity: 0.9937 - miss_rate: 0.1692 - fall_out: 0.0063 - mcc: 0.8700
63/63 [==============================] - 1s 8ms/step - loss: 0.8211 - accuracy: 0.7421 - recall: 0.6800 - precision: 0.8196 - AUROC: 0.9610 - AUPRC: 0.8330 - f1_score: 0.7433 - balanced_accuracy: 0.8317 - specificity: 0.9834 - miss_rate: 0.3200 - fall_out: 0.0166 - mcc: 0.7215
2it [02:33, 76.88s/it]
-- HOLDOUT 3
Model: "CNN_MelS_3s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_20 (Conv2D) (None, 128, 130, 64) 640
max_pooling2d_15 (MaxPoolin (None, 64, 65, 64) 0
g2D)
conv2d_21 (Conv2D) (None, 64, 65, 64) 36928
max_pooling2d_16 (MaxPoolin (None, 32, 33, 64) 0
g2D)
conv2d_22 (Conv2D) (None, 32, 33, 128) 73856
max_pooling2d_17 (MaxPoolin (None, 16, 17, 128) 0
g2D)
conv2d_23 (Conv2D) (None, 16, 17, 128) 262272
flatten_5 (Flatten) (None, 34816) 0
dense_15 (Dense) (None, 128) 4456576
dropout_10 (Dropout) (None, 128) 0
dense_16 (Dense) (None, 128) 16512
dropout_11 (Dropout) (None, 128) 0
dense_17 (Dense) (None, 10) 1290
=================================================================
Total params: 4,848,074
Trainable params: 4,848,074
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 5s 73ms/step - loss: 3.1189 - accuracy: 0.1237 - recall: 0.0071 - precision: 0.1418 - AUROC: 0.5566 - AUPRC: 0.1216 - f1_score: 0.0136 - balanced_accuracy: 0.5012 - specificity: 0.9952 - miss_rate: 0.9929 - fall_out: 0.0048 - mcc: 0.0099 - val_loss: 2.2133 - val_accuracy: 0.1798 - val_recall: 0.0015 - val_precision: 0.5000 - val_AUROC: 0.6541 - val_AUPRC: 0.1850 - val_f1_score: 0.0030 - val_balanced_accuracy: 0.5007 - val_specificity: 0.9998 - val_miss_rate: 0.9985 - val_fall_out: 1.6692e-04 - val_mcc: 0.0231
Epoch 2/100
63/63 [==============================] - 4s 66ms/step - loss: 2.1790 - accuracy: 0.1946 - recall: 0.0233 - precision: 0.4831 - AUROC: 0.6537 - AUPRC: 0.1821 - f1_score: 0.0444 - balanced_accuracy: 0.5103 - specificity: 0.9972 - miss_rate: 0.9767 - fall_out: 0.0028 - mcc: 0.0889 - val_loss: 2.1086 - val_accuracy: 0.2108 - val_recall: 0.0160 - val_precision: 0.5246 - val_AUROC: 0.7354 - val_AUPRC: 0.2342 - val_f1_score: 0.0311 - val_balanced_accuracy: 0.5072 - val_specificity: 0.9984 - val_miss_rate: 0.9840 - val_fall_out: 0.0016 - val_mcc: 0.0783
Epoch 3/100
63/63 [==============================] - 4s 66ms/step - loss: 2.0499 - accuracy: 0.2296 - recall: 0.0400 - precision: 0.5104 - AUROC: 0.7269 - AUPRC: 0.2401 - f1_score: 0.0741 - balanced_accuracy: 0.5178 - specificity: 0.9957 - miss_rate: 0.9600 - fall_out: 0.0043 - mcc: 0.1215 - val_loss: 1.8250 - val_accuracy: 0.3405 - val_recall: 0.0851 - val_precision: 0.6883 - val_AUROC: 0.8039 - val_AUPRC: 0.3372 - val_f1_score: 0.1515 - val_balanced_accuracy: 0.5404 - val_specificity: 0.9957 - val_miss_rate: 0.9149 - val_fall_out: 0.0043 - val_mcc: 0.2194
Epoch 4/100
63/63 [==============================] - 4s 66ms/step - loss: 1.8270 - accuracy: 0.3353 - recall: 0.0939 - precision: 0.5699 - AUROC: 0.8007 - AUPRC: 0.3275 - f1_score: 0.1613 - balanced_accuracy: 0.5430 - specificity: 0.9921 - miss_rate: 0.9061 - fall_out: 0.0079 - mcc: 0.2028 - val_loss: 1.6622 - val_accuracy: 0.4171 - val_recall: 0.1137 - val_precision: 0.6599 - val_AUROC: 0.8491 - val_AUPRC: 0.4203 - val_f1_score: 0.1939 - val_balanced_accuracy: 0.5536 - val_specificity: 0.9935 - val_miss_rate: 0.8863 - val_fall_out: 0.0065 - val_mcc: 0.2471
Epoch 5/100
63/63 [==============================] - 4s 66ms/step - loss: 1.6907 - accuracy: 0.3776 - recall: 0.1341 - precision: 0.5997 - AUROC: 0.8347 - AUPRC: 0.3827 - f1_score: 0.2192 - balanced_accuracy: 0.5621 - specificity: 0.9900 - miss_rate: 0.8659 - fall_out: 0.0100 - mcc: 0.2519 - val_loss: 1.5168 - val_accuracy: 0.4667 - val_recall: 0.2013 - val_precision: 0.6711 - val_AUROC: 0.8683 - val_AUPRC: 0.4772 - val_f1_score: 0.3097 - val_balanced_accuracy: 0.5952 - val_specificity: 0.9890 - val_miss_rate: 0.7987 - val_fall_out: 0.0110 - val_mcc: 0.3348
Epoch 6/100
63/63 [==============================] - 4s 66ms/step - loss: 1.5428 - accuracy: 0.4425 - recall: 0.2005 - precision: 0.6484 - AUROC: 0.8656 - AUPRC: 0.4565 - f1_score: 0.3063 - balanced_accuracy: 0.5942 - specificity: 0.9879 - miss_rate: 0.7995 - fall_out: 0.0121 - mcc: 0.3266 - val_loss: 1.3721 - val_accuracy: 0.5283 - val_recall: 0.2849 - val_precision: 0.7033 - val_AUROC: 0.8927 - val_AUPRC: 0.5700 - val_f1_score: 0.4056 - val_balanced_accuracy: 0.6358 - val_specificity: 0.9866 - val_miss_rate: 0.7151 - val_fall_out: 0.0134 - val_mcc: 0.4132
Epoch 7/100
63/63 [==============================] - 4s 66ms/step - loss: 1.3705 - accuracy: 0.5135 - recall: 0.2908 - precision: 0.7125 - AUROC: 0.8942 - AUPRC: 0.5523 - f1_score: 0.4131 - balanced_accuracy: 0.6389 - specificity: 0.9870 - miss_rate: 0.7092 - fall_out: 0.0130 - mcc: 0.4212 - val_loss: 1.1983 - val_accuracy: 0.6049 - val_recall: 0.3716 - val_precision: 0.8030 - val_AUROC: 0.9201 - val_AUPRC: 0.6636 - val_f1_score: 0.5080 - val_balanced_accuracy: 0.6807 - val_specificity: 0.9899 - val_miss_rate: 0.6284 - val_fall_out: 0.0101 - val_mcc: 0.5162
Epoch 8/100
63/63 [==============================] - 4s 66ms/step - loss: 1.2653 - accuracy: 0.5562 - recall: 0.3541 - precision: 0.7292 - AUROC: 0.9100 - AUPRC: 0.6092 - f1_score: 0.4767 - balanced_accuracy: 0.6697 - specificity: 0.9854 - miss_rate: 0.6459 - fall_out: 0.0146 - mcc: 0.4738 - val_loss: 1.1876 - val_accuracy: 0.5989 - val_recall: 0.3691 - val_precision: 0.8099 - val_AUROC: 0.9220 - val_AUPRC: 0.6690 - val_f1_score: 0.5071 - val_balanced_accuracy: 0.6797 - val_specificity: 0.9904 - val_miss_rate: 0.6309 - val_fall_out: 0.0096 - val_mcc: 0.5170
Epoch 9/100
63/63 [==============================] - 4s 66ms/step - loss: 1.1277 - accuracy: 0.6036 - recall: 0.4210 - precision: 0.7741 - AUROC: 0.9289 - AUPRC: 0.6734 - f1_score: 0.5454 - balanced_accuracy: 0.7037 - specificity: 0.9863 - miss_rate: 0.5790 - fall_out: 0.0137 - mcc: 0.5388 - val_loss: 1.1201 - val_accuracy: 0.6239 - val_recall: 0.4306 - val_precision: 0.7941 - val_AUROC: 0.9296 - val_AUPRC: 0.6860 - val_f1_score: 0.5584 - val_balanced_accuracy: 0.7091 - val_specificity: 0.9876 - val_miss_rate: 0.5694 - val_fall_out: 0.0124 - val_mcc: 0.5540
Epoch 10/100
63/63 [==============================] - 4s 66ms/step - loss: 1.0449 - accuracy: 0.6438 - recall: 0.4704 - precision: 0.7812 - AUROC: 0.9388 - AUPRC: 0.7098 - f1_score: 0.5872 - balanced_accuracy: 0.7279 - specificity: 0.9854 - miss_rate: 0.5296 - fall_out: 0.0146 - mcc: 0.5748 - val_loss: 1.0747 - val_accuracy: 0.6530 - val_recall: 0.4932 - val_precision: 0.7842 - val_AUROC: 0.9345 - val_AUPRC: 0.7191 - val_f1_score: 0.6056 - val_balanced_accuracy: 0.7391 - val_specificity: 0.9849 - val_miss_rate: 0.5068 - val_fall_out: 0.0151 - val_mcc: 0.5909
Epoch 11/100
63/63 [==============================] - 4s 66ms/step - loss: 0.9717 - accuracy: 0.6718 - recall: 0.5262 - precision: 0.8045 - AUROC: 0.9465 - AUPRC: 0.7464 - f1_score: 0.6362 - balanced_accuracy: 0.7560 - specificity: 0.9858 - miss_rate: 0.4738 - fall_out: 0.0142 - mcc: 0.6212 - val_loss: 0.9898 - val_accuracy: 0.6885 - val_recall: 0.5413 - val_precision: 0.8252 - val_AUROC: 0.9448 - val_AUPRC: 0.7603 - val_f1_score: 0.6538 - val_balanced_accuracy: 0.7643 - val_specificity: 0.9873 - val_miss_rate: 0.4587 - val_fall_out: 0.0127 - val_mcc: 0.6405
Epoch 12/100
63/63 [==============================] - 4s 66ms/step - loss: 0.8645 - accuracy: 0.7137 - recall: 0.5763 - precision: 0.8234 - AUROC: 0.9572 - AUPRC: 0.7916 - f1_score: 0.6780 - balanced_accuracy: 0.7813 - specificity: 0.9863 - miss_rate: 0.4237 - fall_out: 0.0137 - mcc: 0.6615 - val_loss: 0.9475 - val_accuracy: 0.7071 - val_recall: 0.5974 - val_precision: 0.8061 - val_AUROC: 0.9496 - val_AUPRC: 0.7783 - val_f1_score: 0.6862 - val_balanced_accuracy: 0.7907 - val_specificity: 0.9840 - val_miss_rate: 0.4026 - val_fall_out: 0.0160 - val_mcc: 0.6659
Epoch 13/100
63/63 [==============================] - 4s 66ms/step - loss: 0.8145 - accuracy: 0.7313 - recall: 0.6195 - precision: 0.8313 - AUROC: 0.9614 - AUPRC: 0.8120 - f1_score: 0.7099 - balanced_accuracy: 0.8028 - specificity: 0.9860 - miss_rate: 0.3805 - fall_out: 0.0140 - mcc: 0.6917 - val_loss: 0.9628 - val_accuracy: 0.6945 - val_recall: 0.5804 - val_precision: 0.7949 - val_AUROC: 0.9471 - val_AUPRC: 0.7641 - val_f1_score: 0.6709 - val_balanced_accuracy: 0.7819 - val_specificity: 0.9834 - val_miss_rate: 0.4196 - val_fall_out: 0.0166 - val_mcc: 0.6501
Epoch 14/100
63/63 [==============================] - 4s 66ms/step - loss: 0.7396 - accuracy: 0.7574 - recall: 0.6475 - precision: 0.8484 - AUROC: 0.9680 - AUPRC: 0.8405 - f1_score: 0.7345 - balanced_accuracy: 0.8173 - specificity: 0.9871 - miss_rate: 0.3525 - fall_out: 0.0129 - mcc: 0.7171 - val_loss: 0.9244 - val_accuracy: 0.7301 - val_recall: 0.6580 - val_precision: 0.8027 - val_AUROC: 0.9522 - val_AUPRC: 0.8009 - val_f1_score: 0.7232 - val_balanced_accuracy: 0.8200 - val_specificity: 0.9820 - val_miss_rate: 0.3420 - val_fall_out: 0.0180 - val_mcc: 0.6999
Epoch 15/100
63/63 [==============================] - 4s 66ms/step - loss: 0.6602 - accuracy: 0.7846 - recall: 0.6973 - precision: 0.8677 - AUROC: 0.9742 - AUPRC: 0.8685 - f1_score: 0.7732 - balanced_accuracy: 0.8427 - specificity: 0.9882 - miss_rate: 0.3027 - fall_out: 0.0118 - mcc: 0.7564 - val_loss: 0.8563 - val_accuracy: 0.7366 - val_recall: 0.6525 - val_precision: 0.8216 - val_AUROC: 0.9574 - val_AUPRC: 0.8150 - val_f1_score: 0.7273 - val_balanced_accuracy: 0.8184 - val_specificity: 0.9843 - val_miss_rate: 0.3475 - val_fall_out: 0.0157 - val_mcc: 0.7065
Epoch 16/100
63/63 [==============================] - 4s 66ms/step - loss: 0.5995 - accuracy: 0.8084 - recall: 0.7296 - precision: 0.8752 - AUROC: 0.9781 - AUPRC: 0.8886 - f1_score: 0.7958 - balanced_accuracy: 0.8590 - specificity: 0.9884 - miss_rate: 0.2704 - fall_out: 0.0116 - mcc: 0.7792 - val_loss: 1.0163 - val_accuracy: 0.7036 - val_recall: 0.6495 - val_precision: 0.7620 - val_AUROC: 0.9443 - val_AUPRC: 0.7727 - val_f1_score: 0.7013 - val_balanced_accuracy: 0.8135 - val_specificity: 0.9775 - val_miss_rate: 0.3505 - val_fall_out: 0.0225 - val_mcc: 0.6736
Epoch 17/100
63/63 [==============================] - 4s 66ms/step - loss: 0.5824 - accuracy: 0.8144 - recall: 0.7455 - precision: 0.8789 - AUROC: 0.9792 - AUPRC: 0.8934 - f1_score: 0.8067 - balanced_accuracy: 0.8670 - specificity: 0.9886 - miss_rate: 0.2545 - fall_out: 0.0114 - mcc: 0.7904 - val_loss: 0.8815 - val_accuracy: 0.7441 - val_recall: 0.6900 - val_precision: 0.8058 - val_AUROC: 0.9542 - val_AUPRC: 0.8144 - val_f1_score: 0.7435 - val_balanced_accuracy: 0.8358 - val_specificity: 0.9815 - val_miss_rate: 0.3100 - val_fall_out: 0.0185 - val_mcc: 0.7200
250/250 [==============================] - 2s 9ms/step - loss: 0.3550 - accuracy: 0.8925 - recall: 0.8389 - precision: 0.9410 - AUROC: 0.9926 - AUPRC: 0.9598 - f1_score: 0.8870 - balanced_accuracy: 0.9165 - specificity: 0.9942 - miss_rate: 0.1611 - fall_out: 0.0058 - mcc: 0.8770
63/63 [==============================] - 1s 9ms/step - loss: 0.8815 - accuracy: 0.7441 - recall: 0.6900 - precision: 0.8058 - AUROC: 0.9542 - AUPRC: 0.8144 - f1_score: 0.7435 - balanced_accuracy: 0.8358 - specificity: 0.9815 - miss_rate: 0.3100 - fall_out: 0.0185 - mcc: 0.7200
3it [03:49, 76.30s/it]
-- HOLDOUT 4
Model: "CNN_MelS_3s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_24 (Conv2D) (None, 128, 130, 64) 640
max_pooling2d_18 (MaxPoolin (None, 64, 65, 64) 0
g2D)
conv2d_25 (Conv2D) (None, 64, 65, 64) 36928
max_pooling2d_19 (MaxPoolin (None, 32, 33, 64) 0
g2D)
conv2d_26 (Conv2D) (None, 32, 33, 128) 73856
max_pooling2d_20 (MaxPoolin (None, 16, 17, 128) 0
g2D)
conv2d_27 (Conv2D) (None, 16, 17, 128) 262272
flatten_6 (Flatten) (None, 34816) 0
dense_18 (Dense) (None, 128) 4456576
dropout_12 (Dropout) (None, 128) 0
dense_19 (Dense) (None, 128) 16512
dropout_13 (Dropout) (None, 128) 0
dense_20 (Dense) (None, 10) 1290
=================================================================
Total params: 4,848,074
Trainable params: 4,848,074
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 6s 74ms/step - loss: 3.0152 - accuracy: 0.1524 - recall: 0.0094 - precision: 0.1708 - AUROC: 0.5800 - AUPRC: 0.1381 - f1_score: 0.0178 - balanced_accuracy: 0.5022 - specificity: 0.9949 - miss_rate: 0.9906 - fall_out: 0.0051 - mcc: 0.0176 - val_loss: 2.0658 - val_accuracy: 0.2519 - val_recall: 0.0601 - val_precision: 0.7018 - val_AUROC: 0.7174 - val_AUPRC: 0.2812 - val_f1_score: 0.1107 - val_balanced_accuracy: 0.5286 - val_specificity: 0.9972 - val_miss_rate: 0.9399 - val_fall_out: 0.0028 - val_mcc: 0.1864
Epoch 2/100
63/63 [==============================] - 4s 66ms/step - loss: 2.0539 - accuracy: 0.2401 - recall: 0.0534 - precision: 0.5591 - AUROC: 0.7183 - AUPRC: 0.2514 - f1_score: 0.0974 - balanced_accuracy: 0.5243 - specificity: 0.9953 - miss_rate: 0.9466 - fall_out: 0.0047 - mcc: 0.1502 - val_loss: 1.8613 - val_accuracy: 0.3225 - val_recall: 0.0671 - val_precision: 0.7614 - val_AUROC: 0.8003 - val_AUPRC: 0.3515 - val_f1_score: 0.1233 - val_balanced_accuracy: 0.5324 - val_specificity: 0.9977 - val_miss_rate: 0.9329 - val_fall_out: 0.0023 - val_mcc: 0.2079
Epoch 3/100
63/63 [==============================] - 4s 66ms/step - loss: 1.9461 - accuracy: 0.2848 - recall: 0.0762 - precision: 0.5720 - AUROC: 0.7681 - AUPRC: 0.2935 - f1_score: 0.1344 - balanced_accuracy: 0.5349 - specificity: 0.9937 - miss_rate: 0.9238 - fall_out: 0.0063 - mcc: 0.1827 - val_loss: 1.7580 - val_accuracy: 0.3796 - val_recall: 0.0741 - val_precision: 0.7749 - val_AUROC: 0.8407 - val_AUPRC: 0.4023 - val_f1_score: 0.1353 - val_balanced_accuracy: 0.5359 - val_specificity: 0.9976 - val_miss_rate: 0.9259 - val_fall_out: 0.0024 - val_mcc: 0.2211
Epoch 4/100
63/63 [==============================] - 4s 66ms/step - loss: 1.8010 - accuracy: 0.3355 - recall: 0.1150 - precision: 0.6124 - AUROC: 0.8048 - AUPRC: 0.3530 - f1_score: 0.1936 - balanced_accuracy: 0.5534 - specificity: 0.9919 - miss_rate: 0.8850 - fall_out: 0.0081 - mcc: 0.2363 - val_loss: 1.5465 - val_accuracy: 0.4737 - val_recall: 0.1417 - val_precision: 0.7408 - val_AUROC: 0.8793 - val_AUPRC: 0.5001 - val_f1_score: 0.2379 - val_balanced_accuracy: 0.5681 - val_specificity: 0.9945 - val_miss_rate: 0.8583 - val_fall_out: 0.0055 - val_mcc: 0.2983
Epoch 5/100
63/63 [==============================] - 4s 66ms/step - loss: 1.6410 - accuracy: 0.4091 - recall: 0.1801 - precision: 0.6695 - AUROC: 0.8444 - AUPRC: 0.4368 - f1_score: 0.2839 - balanced_accuracy: 0.5851 - specificity: 0.9901 - miss_rate: 0.8199 - fall_out: 0.0099 - mcc: 0.3156 - val_loss: 1.3294 - val_accuracy: 0.5603 - val_recall: 0.2689 - val_precision: 0.8124 - val_AUROC: 0.9109 - val_AUPRC: 0.6123 - val_f1_score: 0.4041 - val_balanced_accuracy: 0.6310 - val_specificity: 0.9931 - val_miss_rate: 0.7311 - val_fall_out: 0.0069 - val_mcc: 0.4394
Epoch 6/100
63/63 [==============================] - 4s 66ms/step - loss: 1.4945 - accuracy: 0.4858 - recall: 0.2516 - precision: 0.7165 - AUROC: 0.8733 - AUPRC: 0.5124 - f1_score: 0.3725 - balanced_accuracy: 0.6203 - specificity: 0.9889 - miss_rate: 0.7484 - fall_out: 0.0111 - mcc: 0.3920 - val_loss: 1.3659 - val_accuracy: 0.5508 - val_recall: 0.2729 - val_precision: 0.7968 - val_AUROC: 0.8944 - val_AUPRC: 0.5893 - val_f1_score: 0.4066 - val_balanced_accuracy: 0.6326 - val_specificity: 0.9923 - val_miss_rate: 0.7271 - val_fall_out: 0.0077 - val_mcc: 0.4374
Epoch 7/100
63/63 [==============================] - 4s 66ms/step - loss: 1.3771 - accuracy: 0.5289 - recall: 0.3044 - precision: 0.7406 - AUROC: 0.8933 - AUPRC: 0.5732 - f1_score: 0.4314 - balanced_accuracy: 0.6463 - specificity: 0.9882 - miss_rate: 0.6956 - fall_out: 0.0118 - mcc: 0.4421 - val_loss: 1.1460 - val_accuracy: 0.6199 - val_recall: 0.3761 - val_precision: 0.8041 - val_AUROC: 0.9273 - val_AUPRC: 0.6780 - val_f1_score: 0.5125 - val_balanced_accuracy: 0.6829 - val_specificity: 0.9898 - val_miss_rate: 0.6239 - val_fall_out: 0.0102 - val_mcc: 0.5199
Epoch 8/100
63/63 [==============================] - 4s 66ms/step - loss: 1.2771 - accuracy: 0.5680 - recall: 0.3561 - precision: 0.7602 - AUROC: 0.9081 - AUPRC: 0.6216 - f1_score: 0.4850 - balanced_accuracy: 0.6718 - specificity: 0.9875 - miss_rate: 0.6439 - fall_out: 0.0125 - mcc: 0.4878 - val_loss: 1.1460 - val_accuracy: 0.6159 - val_recall: 0.3886 - val_precision: 0.8126 - val_AUROC: 0.9269 - val_AUPRC: 0.6834 - val_f1_score: 0.5257 - val_balanced_accuracy: 0.6893 - val_specificity: 0.9900 - val_miss_rate: 0.6114 - val_fall_out: 0.0100 - val_mcc: 0.5323
Epoch 9/100
63/63 [==============================] - 4s 66ms/step - loss: 1.1494 - accuracy: 0.6080 - recall: 0.4248 - precision: 0.7852 - AUROC: 0.9252 - AUPRC: 0.6792 - f1_score: 0.5514 - balanced_accuracy: 0.7060 - specificity: 0.9871 - miss_rate: 0.5752 - fall_out: 0.0129 - mcc: 0.5463 - val_loss: 1.0137 - val_accuracy: 0.6720 - val_recall: 0.4812 - val_precision: 0.8270 - val_AUROC: 0.9432 - val_AUPRC: 0.7390 - val_f1_score: 0.6084 - val_balanced_accuracy: 0.7350 - val_specificity: 0.9888 - val_miss_rate: 0.5188 - val_fall_out: 0.0112 - val_mcc: 0.6024
Epoch 10/100
63/63 [==============================] - 4s 66ms/step - loss: 1.0123 - accuracy: 0.6645 - recall: 0.5014 - precision: 0.8074 - AUROC: 0.9419 - AUPRC: 0.7399 - f1_score: 0.6186 - balanced_accuracy: 0.7440 - specificity: 0.9867 - miss_rate: 0.4986 - fall_out: 0.0133 - mcc: 0.6067 - val_loss: 0.9275 - val_accuracy: 0.7081 - val_recall: 0.5483 - val_precision: 0.8397 - val_AUROC: 0.9509 - val_AUPRC: 0.7783 - val_f1_score: 0.6634 - val_balanced_accuracy: 0.7683 - val_specificity: 0.9884 - val_miss_rate: 0.4517 - val_fall_out: 0.0116 - val_mcc: 0.6517
Epoch 11/100
63/63 [==============================] - 4s 66ms/step - loss: 0.9980 - accuracy: 0.6682 - recall: 0.5190 - precision: 0.8177 - AUROC: 0.9435 - AUPRC: 0.7464 - f1_score: 0.6350 - balanced_accuracy: 0.7531 - specificity: 0.9871 - miss_rate: 0.4810 - fall_out: 0.0129 - mcc: 0.6228 - val_loss: 0.8961 - val_accuracy: 0.6955 - val_recall: 0.5704 - val_precision: 0.8118 - val_AUROC: 0.9536 - val_AUPRC: 0.7792 - val_f1_score: 0.6700 - val_balanced_accuracy: 0.7778 - val_specificity: 0.9853 - val_miss_rate: 0.4296 - val_fall_out: 0.0147 - val_mcc: 0.6523
Epoch 12/100
63/63 [==============================] - 4s 66ms/step - loss: 0.9325 - accuracy: 0.6935 - recall: 0.5476 - precision: 0.8291 - AUROC: 0.9500 - AUPRC: 0.7732 - f1_score: 0.6596 - balanced_accuracy: 0.7675 - specificity: 0.9875 - miss_rate: 0.4524 - fall_out: 0.0125 - mcc: 0.6463 - val_loss: 0.8704 - val_accuracy: 0.7156 - val_recall: 0.6174 - val_precision: 0.8231 - val_AUROC: 0.9545 - val_AUPRC: 0.7923 - val_f1_score: 0.7056 - val_balanced_accuracy: 0.8013 - val_specificity: 0.9853 - val_miss_rate: 0.3826 - val_fall_out: 0.0147 - val_mcc: 0.6864
Epoch 13/100
63/63 [==============================] - 4s 66ms/step - loss: 0.8380 - accuracy: 0.7265 - recall: 0.6042 - precision: 0.8414 - AUROC: 0.9596 - AUPRC: 0.8078 - f1_score: 0.7034 - balanced_accuracy: 0.7958 - specificity: 0.9873 - miss_rate: 0.3958 - fall_out: 0.0127 - mcc: 0.6874 - val_loss: 0.9256 - val_accuracy: 0.6950 - val_recall: 0.5769 - val_precision: 0.8130 - val_AUROC: 0.9501 - val_AUPRC: 0.7747 - val_f1_score: 0.6749 - val_balanced_accuracy: 0.7811 - val_specificity: 0.9853 - val_miss_rate: 0.4231 - val_fall_out: 0.0147 - val_mcc: 0.6568
Epoch 14/100
63/63 [==============================] - 4s 66ms/step - loss: 0.8080 - accuracy: 0.7368 - recall: 0.6219 - precision: 0.8493 - AUROC: 0.9624 - AUPRC: 0.8211 - f1_score: 0.7180 - balanced_accuracy: 0.8048 - specificity: 0.9877 - miss_rate: 0.3781 - fall_out: 0.0123 - mcc: 0.7020 - val_loss: 0.9297 - val_accuracy: 0.6915 - val_recall: 0.5648 - val_precision: 0.8174 - val_AUROC: 0.9505 - val_AUPRC: 0.7721 - val_f1_score: 0.6680 - val_balanced_accuracy: 0.7754 - val_specificity: 0.9860 - val_miss_rate: 0.4352 - val_fall_out: 0.0140 - val_mcc: 0.6515
250/250 [==============================] - 2s 8ms/step - loss: 0.5875 - accuracy: 0.8106 - recall: 0.6901 - precision: 0.9171 - AUROC: 0.9827 - AUPRC: 0.9015 - f1_score: 0.7876 - balanced_accuracy: 0.8416 - specificity: 0.9931 - miss_rate: 0.3099 - fall_out: 0.0069 - mcc: 0.7770
63/63 [==============================] - 1s 8ms/step - loss: 0.9297 - accuracy: 0.6915 - recall: 0.5648 - precision: 0.8174 - AUROC: 0.9505 - AUPRC: 0.7721 - f1_score: 0.6680 - balanced_accuracy: 0.7754 - specificity: 0.9860 - miss_rate: 0.4352 - fall_out: 0.0140 - mcc: 0.6515
4it [04:52, 71.13s/it]
-- HOLDOUT 5
Model: "CNN_MelS_3s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_28 (Conv2D) (None, 128, 130, 64) 640
max_pooling2d_21 (MaxPoolin (None, 64, 65, 64) 0
g2D)
conv2d_29 (Conv2D) (None, 64, 65, 64) 36928
max_pooling2d_22 (MaxPoolin (None, 32, 33, 64) 0
g2D)
conv2d_30 (Conv2D) (None, 32, 33, 128) 73856
max_pooling2d_23 (MaxPoolin (None, 16, 17, 128) 0
g2D)
conv2d_31 (Conv2D) (None, 16, 17, 128) 262272
flatten_7 (Flatten) (None, 34816) 0
dense_21 (Dense) (None, 128) 4456576
dropout_14 (Dropout) (None, 128) 0
dense_22 (Dense) (None, 128) 16512
dropout_15 (Dropout) (None, 128) 0
dense_23 (Dense) (None, 10) 1290
=================================================================
Total params: 4,848,074
Trainable params: 4,848,074
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 6s 74ms/step - loss: 2.7922 - accuracy: 0.1662 - recall: 0.0119 - precision: 0.2164 - AUROC: 0.5803 - AUPRC: 0.1365 - f1_score: 0.0226 - balanced_accuracy: 0.5036 - specificity: 0.9952 - miss_rate: 0.9881 - fall_out: 0.0048 - mcc: 0.0289 - val_loss: 2.0494 - val_accuracy: 0.2298 - val_recall: 0.0511 - val_precision: 0.7669 - val_AUROC: 0.7284 - val_AUPRC: 0.2737 - val_f1_score: 0.0958 - val_balanced_accuracy: 0.5247 - val_specificity: 0.9983 - val_miss_rate: 0.9489 - val_fall_out: 0.0017 - val_mcc: 0.1820
Epoch 2/100
63/63 [==============================] - 4s 66ms/step - loss: 2.0745 - accuracy: 0.2290 - recall: 0.0545 - precision: 0.5657 - AUROC: 0.7016 - AUPRC: 0.2434 - f1_score: 0.0994 - balanced_accuracy: 0.5249 - specificity: 0.9954 - miss_rate: 0.9455 - fall_out: 0.0046 - mcc: 0.1531 - val_loss: 1.9532 - val_accuracy: 0.2679 - val_recall: 0.0711 - val_precision: 0.6605 - val_AUROC: 0.7584 - val_AUPRC: 0.3032 - val_f1_score: 0.1284 - val_balanced_accuracy: 0.5335 - val_specificity: 0.9959 - val_miss_rate: 0.9289 - val_fall_out: 0.0041 - val_mcc: 0.1949
Epoch 3/100
63/63 [==============================] - 4s 66ms/step - loss: 1.9365 - accuracy: 0.2729 - recall: 0.0810 - precision: 0.5974 - AUROC: 0.7621 - AUPRC: 0.2971 - f1_score: 0.1427 - balanced_accuracy: 0.5375 - specificity: 0.9939 - miss_rate: 0.9190 - fall_out: 0.0061 - mcc: 0.1944 - val_loss: 1.7414 - val_accuracy: 0.3650 - val_recall: 0.1312 - val_precision: 0.6550 - val_AUROC: 0.8312 - val_AUPRC: 0.3921 - val_f1_score: 0.2186 - val_balanced_accuracy: 0.5618 - val_specificity: 0.9923 - val_miss_rate: 0.8688 - val_fall_out: 0.0077 - val_mcc: 0.2645
Epoch 4/100
63/63 [==============================] - 4s 66ms/step - loss: 1.8074 - accuracy: 0.3340 - recall: 0.1126 - precision: 0.6204 - AUROC: 0.8058 - AUPRC: 0.3458 - f1_score: 0.1906 - balanced_accuracy: 0.5525 - specificity: 0.9923 - miss_rate: 0.8874 - fall_out: 0.0077 - mcc: 0.2359 - val_loss: 1.5715 - val_accuracy: 0.4547 - val_recall: 0.1362 - val_precision: 0.7662 - val_AUROC: 0.8666 - val_AUPRC: 0.4871 - val_f1_score: 0.2313 - val_balanced_accuracy: 0.5658 - val_specificity: 0.9954 - val_miss_rate: 0.8638 - val_fall_out: 0.0046 - val_mcc: 0.2987
Epoch 5/100
63/63 [==============================] - 4s 66ms/step - loss: 1.6416 - accuracy: 0.4123 - recall: 0.1671 - precision: 0.6565 - AUROC: 0.8446 - AUPRC: 0.4257 - f1_score: 0.2664 - balanced_accuracy: 0.5787 - specificity: 0.9903 - miss_rate: 0.8329 - fall_out: 0.0097 - mcc: 0.2998 - val_loss: 1.3380 - val_accuracy: 0.5403 - val_recall: 0.2824 - val_precision: 0.7203 - val_AUROC: 0.9020 - val_AUPRC: 0.5777 - val_f1_score: 0.4058 - val_balanced_accuracy: 0.6351 - val_specificity: 0.9878 - val_miss_rate: 0.7176 - val_fall_out: 0.0122 - val_mcc: 0.4177
Epoch 6/100
63/63 [==============================] - 4s 66ms/step - loss: 1.4860 - accuracy: 0.4729 - recall: 0.2489 - precision: 0.6847 - AUROC: 0.8742 - AUPRC: 0.5034 - f1_score: 0.3651 - balanced_accuracy: 0.6181 - specificity: 0.9873 - miss_rate: 0.7511 - fall_out: 0.0127 - mcc: 0.3785 - val_loss: 1.3006 - val_accuracy: 0.5523 - val_recall: 0.3035 - val_precision: 0.7710 - val_AUROC: 0.9079 - val_AUPRC: 0.6063 - val_f1_score: 0.4355 - val_balanced_accuracy: 0.6467 - val_specificity: 0.9900 - val_miss_rate: 0.6965 - val_fall_out: 0.0100 - val_mcc: 0.4527
Epoch 7/100
63/63 [==============================] - 4s 66ms/step - loss: 1.3470 - accuracy: 0.5267 - recall: 0.3116 - precision: 0.7256 - AUROC: 0.8981 - AUPRC: 0.5754 - f1_score: 0.4360 - balanced_accuracy: 0.6493 - specificity: 0.9869 - miss_rate: 0.6884 - fall_out: 0.0131 - mcc: 0.4417 - val_loss: 1.1652 - val_accuracy: 0.6139 - val_recall: 0.3490 - val_precision: 0.8095 - val_AUROC: 0.9259 - val_AUPRC: 0.6715 - val_f1_score: 0.4878 - val_balanced_accuracy: 0.6699 - val_specificity: 0.9909 - val_miss_rate: 0.6510 - val_fall_out: 0.0091 - val_mcc: 0.5020
Epoch 8/100
63/63 [==============================] - 4s 66ms/step - loss: 1.1861 - accuracy: 0.5971 - recall: 0.3949 - precision: 0.7651 - AUROC: 0.9209 - AUPRC: 0.6512 - f1_score: 0.5209 - balanced_accuracy: 0.6907 - specificity: 0.9865 - miss_rate: 0.6051 - fall_out: 0.0135 - mcc: 0.5172 - val_loss: 0.9917 - val_accuracy: 0.6695 - val_recall: 0.4712 - val_precision: 0.8462 - val_AUROC: 0.9464 - val_AUPRC: 0.7472 - val_f1_score: 0.6053 - val_balanced_accuracy: 0.7308 - val_specificity: 0.9905 - val_miss_rate: 0.5288 - val_fall_out: 0.0095 - val_mcc: 0.6040
Epoch 9/100
63/63 [==============================] - 4s 68ms/step - loss: 1.0731 - accuracy: 0.6435 - recall: 0.4729 - precision: 0.7988 - AUROC: 0.9350 - AUPRC: 0.7101 - f1_score: 0.5941 - balanced_accuracy: 0.7299 - specificity: 0.9868 - miss_rate: 0.5271 - fall_out: 0.0132 - mcc: 0.5844 - val_loss: 0.9506 - val_accuracy: 0.6910 - val_recall: 0.4867 - val_precision: 0.8556 - val_AUROC: 0.9504 - val_AUPRC: 0.7692 - val_f1_score: 0.6205 - val_balanced_accuracy: 0.7388 - val_specificity: 0.9909 - val_miss_rate: 0.5133 - val_fall_out: 0.0091 - val_mcc: 0.6186
Epoch 10/100
63/63 [==============================] - 4s 68ms/step - loss: 0.9608 - accuracy: 0.6765 - recall: 0.5212 - precision: 0.8105 - AUROC: 0.9473 - AUPRC: 0.7556 - f1_score: 0.6344 - balanced_accuracy: 0.7538 - specificity: 0.9865 - miss_rate: 0.4788 - fall_out: 0.0135 - mcc: 0.6208 - val_loss: 0.8969 - val_accuracy: 0.7026 - val_recall: 0.5829 - val_precision: 0.8157 - val_AUROC: 0.9529 - val_AUPRC: 0.7897 - val_f1_score: 0.6799 - val_balanced_accuracy: 0.7841 - val_specificity: 0.9854 - val_miss_rate: 0.4171 - val_fall_out: 0.0146 - val_mcc: 0.6618
Epoch 11/100
63/63 [==============================] - 4s 68ms/step - loss: 0.9140 - accuracy: 0.6966 - recall: 0.5635 - precision: 0.8210 - AUROC: 0.9519 - AUPRC: 0.7776 - f1_score: 0.6683 - balanced_accuracy: 0.7749 - specificity: 0.9863 - miss_rate: 0.4365 - fall_out: 0.0137 - mcc: 0.6524 - val_loss: 0.8905 - val_accuracy: 0.7006 - val_recall: 0.5899 - val_precision: 0.8221 - val_AUROC: 0.9523 - val_AUPRC: 0.7900 - val_f1_score: 0.6869 - val_balanced_accuracy: 0.7878 - val_specificity: 0.9858 - val_miss_rate: 0.4101 - val_fall_out: 0.0142 - val_mcc: 0.6692
Epoch 12/100
63/63 [==============================] - 4s 68ms/step - loss: 0.8279 - accuracy: 0.7267 - recall: 0.6076 - precision: 0.8438 - AUROC: 0.9608 - AUPRC: 0.8127 - f1_score: 0.7065 - balanced_accuracy: 0.7975 - specificity: 0.9875 - miss_rate: 0.3924 - fall_out: 0.0125 - mcc: 0.6906 - val_loss: 0.8240 - val_accuracy: 0.7391 - val_recall: 0.6430 - val_precision: 0.8414 - val_AUROC: 0.9616 - val_AUPRC: 0.8266 - val_f1_score: 0.7289 - val_balanced_accuracy: 0.8147 - val_specificity: 0.9865 - val_miss_rate: 0.3570 - val_fall_out: 0.0135 - val_mcc: 0.7109
Epoch 13/100
63/63 [==============================] - 4s 66ms/step - loss: 0.7528 - accuracy: 0.7550 - recall: 0.6512 - precision: 0.8583 - AUROC: 0.9662 - AUPRC: 0.8393 - f1_score: 0.7405 - balanced_accuracy: 0.8196 - specificity: 0.9881 - miss_rate: 0.3488 - fall_out: 0.0119 - mcc: 0.7243 - val_loss: 0.8399 - val_accuracy: 0.7291 - val_recall: 0.6114 - val_precision: 0.8427 - val_AUROC: 0.9586 - val_AUPRC: 0.8124 - val_f1_score: 0.7086 - val_balanced_accuracy: 0.7994 - val_specificity: 0.9873 - val_miss_rate: 0.3886 - val_fall_out: 0.0127 - val_mcc: 0.6924
Epoch 14/100
63/63 [==============================] - 4s 66ms/step - loss: 0.6762 - accuracy: 0.7823 - recall: 0.6889 - precision: 0.8663 - AUROC: 0.9731 - AUPRC: 0.8648 - f1_score: 0.7675 - balanced_accuracy: 0.8385 - specificity: 0.9882 - miss_rate: 0.3111 - fall_out: 0.0118 - mcc: 0.7508 - val_loss: 0.8776 - val_accuracy: 0.7101 - val_recall: 0.6294 - val_precision: 0.8136 - val_AUROC: 0.9555 - val_AUPRC: 0.8032 - val_f1_score: 0.7098 - val_balanced_accuracy: 0.8067 - val_specificity: 0.9840 - val_miss_rate: 0.3706 - val_fall_out: 0.0160 - val_mcc: 0.6888
250/250 [==============================] - 2s 8ms/step - loss: 0.4863 - accuracy: 0.8409 - recall: 0.7632 - precision: 0.9236 - AUROC: 0.9867 - AUPRC: 0.9253 - f1_score: 0.8357 - balanced_accuracy: 0.8781 - specificity: 0.9930 - miss_rate: 0.2368 - fall_out: 0.0070 - mcc: 0.8239
63/63 [==============================] - 1s 8ms/step - loss: 0.8776 - accuracy: 0.7101 - recall: 0.6294 - precision: 0.8136 - AUROC: 0.9555 - AUPRC: 0.8032 - f1_score: 0.7098 - balanced_accuracy: 0.8067 - specificity: 0.9840 - miss_rate: 0.3706 - fall_out: 0.0160 - mcc: 0.6888
5it [05:57, 68.65s/it]
-- HOLDOUT 6
Model: "CNN_MelS_3s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_32 (Conv2D) (None, 128, 130, 64) 640
max_pooling2d_24 (MaxPoolin (None, 64, 65, 64) 0
g2D)
conv2d_33 (Conv2D) (None, 64, 65, 64) 36928
max_pooling2d_25 (MaxPoolin (None, 32, 33, 64) 0
g2D)
conv2d_34 (Conv2D) (None, 32, 33, 128) 73856
max_pooling2d_26 (MaxPoolin (None, 16, 17, 128) 0
g2D)
conv2d_35 (Conv2D) (None, 16, 17, 128) 262272
flatten_8 (Flatten) (None, 34816) 0
dense_24 (Dense) (None, 128) 4456576
dropout_16 (Dropout) (None, 128) 0
dense_25 (Dense) (None, 128) 16512
dropout_17 (Dropout) (None, 128) 0
dense_26 (Dense) (None, 10) 1290
=================================================================
Total params: 4,848,074
Trainable params: 4,848,074
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 6s 74ms/step - loss: 2.8561 - accuracy: 0.1589 - recall: 0.0153 - precision: 0.2383 - AUROC: 0.6010 - AUPRC: 0.1479 - f1_score: 0.0287 - balanced_accuracy: 0.5049 - specificity: 0.9946 - miss_rate: 0.9847 - fall_out: 0.0054 - mcc: 0.0370 - val_loss: 2.0233 - val_accuracy: 0.2534 - val_recall: 0.0085 - val_precision: 0.5667 - val_AUROC: 0.7346 - val_AUPRC: 0.2737 - val_f1_score: 0.0168 - val_balanced_accuracy: 0.5039 - val_specificity: 0.9993 - val_miss_rate: 0.9915 - val_fall_out: 7.2331e-04 - val_mcc: 0.0603
Epoch 2/100
63/63 [==============================] - 4s 66ms/step - loss: 2.0212 - accuracy: 0.2568 - recall: 0.0527 - precision: 0.5296 - AUROC: 0.7283 - AUPRC: 0.2573 - f1_score: 0.0959 - balanced_accuracy: 0.5238 - specificity: 0.9948 - miss_rate: 0.9473 - fall_out: 0.0052 - mcc: 0.1436 - val_loss: 1.8495 - val_accuracy: 0.3315 - val_recall: 0.1032 - val_precision: 0.6519 - val_AUROC: 0.8085 - val_AUPRC: 0.3418 - val_f1_score: 0.1781 - val_balanced_accuracy: 0.5485 - val_specificity: 0.9939 - val_miss_rate: 0.8968 - val_fall_out: 0.0061 - val_mcc: 0.2333
Epoch 3/100
63/63 [==============================] - 4s 66ms/step - loss: 1.8369 - accuracy: 0.3268 - recall: 0.1025 - precision: 0.5906 - AUROC: 0.7961 - AUPRC: 0.3358 - f1_score: 0.1746 - balanced_accuracy: 0.5473 - specificity: 0.9921 - miss_rate: 0.8975 - fall_out: 0.0079 - mcc: 0.2173 - val_loss: 1.6490 - val_accuracy: 0.4432 - val_recall: 0.1287 - val_precision: 0.7219 - val_AUROC: 0.8518 - val_AUPRC: 0.4445 - val_f1_score: 0.2184 - val_balanced_accuracy: 0.5616 - val_specificity: 0.9945 - val_miss_rate: 0.8713 - val_fall_out: 0.0055 - val_mcc: 0.2793
Epoch 4/100
63/63 [==============================] - 4s 66ms/step - loss: 1.6756 - accuracy: 0.3987 - recall: 0.1617 - precision: 0.6350 - AUROC: 0.8371 - AUPRC: 0.4124 - f1_score: 0.2578 - balanced_accuracy: 0.5757 - specificity: 0.9897 - miss_rate: 0.8383 - fall_out: 0.0103 - mcc: 0.2883 - val_loss: 1.5190 - val_accuracy: 0.4907 - val_recall: 0.2088 - val_precision: 0.7913 - val_AUROC: 0.8699 - val_AUPRC: 0.5178 - val_f1_score: 0.3304 - val_balanced_accuracy: 0.6013 - val_specificity: 0.9939 - val_miss_rate: 0.7912 - val_fall_out: 0.0061 - val_mcc: 0.3794
Epoch 5/100
63/63 [==============================] - 4s 66ms/step - loss: 1.4897 - accuracy: 0.4744 - recall: 0.2467 - precision: 0.6964 - AUROC: 0.8731 - AUPRC: 0.5100 - f1_score: 0.3644 - balanced_accuracy: 0.6174 - specificity: 0.9880 - miss_rate: 0.7533 - fall_out: 0.0120 - mcc: 0.3810 - val_loss: 1.2485 - val_accuracy: 0.5894 - val_recall: 0.3220 - val_precision: 0.8427 - val_AUROC: 0.9148 - val_AUPRC: 0.6550 - val_f1_score: 0.4659 - val_balanced_accuracy: 0.6577 - val_specificity: 0.9933 - val_miss_rate: 0.6780 - val_fall_out: 0.0067 - val_mcc: 0.4934
Epoch 6/100
63/63 [==============================] - 4s 66ms/step - loss: 1.3369 - accuracy: 0.5418 - recall: 0.3254 - precision: 0.7395 - AUROC: 0.8997 - AUPRC: 0.5882 - f1_score: 0.4519 - balanced_accuracy: 0.6563 - specificity: 0.9873 - miss_rate: 0.6746 - fall_out: 0.0127 - mcc: 0.4573 - val_loss: 1.1305 - val_accuracy: 0.6094 - val_recall: 0.4186 - val_precision: 0.8212 - val_AUROC: 0.9288 - val_AUPRC: 0.6953 - val_f1_score: 0.5546 - val_balanced_accuracy: 0.7043 - val_specificity: 0.9899 - val_miss_rate: 0.5814 - val_fall_out: 0.0101 - val_mcc: 0.5572
Epoch 7/100
63/63 [==============================] - 4s 66ms/step - loss: 1.1980 - accuracy: 0.5966 - recall: 0.4023 - precision: 0.7714 - AUROC: 0.9190 - AUPRC: 0.6542 - f1_score: 0.5288 - balanced_accuracy: 0.6945 - specificity: 0.9868 - miss_rate: 0.5977 - fall_out: 0.0132 - mcc: 0.5250 - val_loss: 1.0678 - val_accuracy: 0.6425 - val_recall: 0.4677 - val_precision: 0.8530 - val_AUROC: 0.9356 - val_AUPRC: 0.7266 - val_f1_score: 0.6041 - val_balanced_accuracy: 0.7294 - val_specificity: 0.9910 - val_miss_rate: 0.5323 - val_fall_out: 0.0090 - val_mcc: 0.6045
Epoch 8/100
63/63 [==============================] - 4s 66ms/step - loss: 1.0787 - accuracy: 0.6319 - recall: 0.4605 - precision: 0.7926 - AUROC: 0.9347 - AUPRC: 0.7129 - f1_score: 0.5826 - balanced_accuracy: 0.7236 - specificity: 0.9866 - miss_rate: 0.5395 - fall_out: 0.0134 - mcc: 0.5734 - val_loss: 0.9920 - val_accuracy: 0.6675 - val_recall: 0.5078 - val_precision: 0.8422 - val_AUROC: 0.9432 - val_AUPRC: 0.7570 - val_f1_score: 0.6336 - val_balanced_accuracy: 0.7486 - val_specificity: 0.9894 - val_miss_rate: 0.4922 - val_fall_out: 0.0106 - val_mcc: 0.6266
Epoch 9/100
63/63 [==============================] - 4s 66ms/step - loss: 1.0162 - accuracy: 0.6651 - recall: 0.5044 - precision: 0.8081 - AUROC: 0.9425 - AUPRC: 0.7417 - f1_score: 0.6211 - balanced_accuracy: 0.7455 - specificity: 0.9867 - miss_rate: 0.4956 - fall_out: 0.0133 - mcc: 0.6090 - val_loss: 0.9650 - val_accuracy: 0.6890 - val_recall: 0.5633 - val_precision: 0.8303 - val_AUROC: 0.9461 - val_AUPRC: 0.7660 - val_f1_score: 0.6712 - val_balanced_accuracy: 0.7753 - val_specificity: 0.9872 - val_miss_rate: 0.4367 - val_fall_out: 0.0128 - val_mcc: 0.6567
Epoch 10/100
63/63 [==============================] - 4s 66ms/step - loss: 0.9148 - accuracy: 0.6969 - recall: 0.5631 - precision: 0.8311 - AUROC: 0.9519 - AUPRC: 0.7803 - f1_score: 0.6713 - balanced_accuracy: 0.7752 - specificity: 0.9873 - miss_rate: 0.4369 - fall_out: 0.0127 - mcc: 0.6570 - val_loss: 0.9229 - val_accuracy: 0.7166 - val_recall: 0.5643 - val_precision: 0.8525 - val_AUROC: 0.9509 - val_AUPRC: 0.7931 - val_f1_score: 0.6791 - val_balanced_accuracy: 0.7767 - val_specificity: 0.9892 - val_miss_rate: 0.4357 - val_fall_out: 0.0108 - val_mcc: 0.6679
Epoch 11/100
63/63 [==============================] - 4s 66ms/step - loss: 0.8195 - accuracy: 0.7282 - recall: 0.6125 - precision: 0.8385 - AUROC: 0.9609 - AUPRC: 0.8144 - f1_score: 0.7079 - balanced_accuracy: 0.7997 - specificity: 0.9869 - miss_rate: 0.3875 - fall_out: 0.0131 - mcc: 0.6910 - val_loss: 0.9510 - val_accuracy: 0.6980 - val_recall: 0.6009 - val_precision: 0.8021 - val_AUROC: 0.9471 - val_AUPRC: 0.7792 - val_f1_score: 0.6871 - val_balanced_accuracy: 0.7922 - val_specificity: 0.9835 - val_miss_rate: 0.3991 - val_fall_out: 0.0165 - val_mcc: 0.6660
Epoch 12/100
63/63 [==============================] - 4s 66ms/step - loss: 0.7425 - accuracy: 0.7608 - recall: 0.6498 - precision: 0.8595 - AUROC: 0.9675 - AUPRC: 0.8431 - f1_score: 0.7401 - balanced_accuracy: 0.8190 - specificity: 0.9882 - miss_rate: 0.3502 - fall_out: 0.0118 - mcc: 0.7240 - val_loss: 0.8117 - val_accuracy: 0.7516 - val_recall: 0.6425 - val_precision: 0.8628 - val_AUROC: 0.9600 - val_AUPRC: 0.8289 - val_f1_score: 0.7365 - val_balanced_accuracy: 0.8156 - val_specificity: 0.9886 - val_miss_rate: 0.3575 - val_fall_out: 0.0114 - val_mcc: 0.7212
Epoch 13/100
63/63 [==============================] - 4s 66ms/step - loss: 0.6872 - accuracy: 0.7788 - recall: 0.6819 - precision: 0.8637 - AUROC: 0.9717 - AUPRC: 0.8625 - f1_score: 0.7621 - balanced_accuracy: 0.8350 - specificity: 0.9880 - miss_rate: 0.3181 - fall_out: 0.0120 - mcc: 0.7453 - val_loss: 0.8830 - val_accuracy: 0.7191 - val_recall: 0.6350 - val_precision: 0.8175 - val_AUROC: 0.9544 - val_AUPRC: 0.8018 - val_f1_score: 0.7148 - val_balanced_accuracy: 0.8096 - val_specificity: 0.9843 - val_miss_rate: 0.3650 - val_fall_out: 0.0157 - val_mcc: 0.6941
Epoch 14/100
63/63 [==============================] - 4s 66ms/step - loss: 0.6992 - accuracy: 0.7687 - recall: 0.6718 - precision: 0.8625 - AUROC: 0.9712 - AUPRC: 0.8563 - f1_score: 0.7553 - balanced_accuracy: 0.8300 - specificity: 0.9881 - miss_rate: 0.3282 - fall_out: 0.0119 - mcc: 0.7387 - val_loss: 0.8544 - val_accuracy: 0.7466 - val_recall: 0.6810 - val_precision: 0.8086 - val_AUROC: 0.9562 - val_AUPRC: 0.8195 - val_f1_score: 0.7393 - val_balanced_accuracy: 0.8316 - val_specificity: 0.9821 - val_miss_rate: 0.3190 - val_fall_out: 0.0179 - val_mcc: 0.7163
250/250 [==============================] - 2s 9ms/step - loss: 0.4132 - accuracy: 0.8656 - recall: 0.8155 - precision: 0.9131 - AUROC: 0.9896 - AUPRC: 0.9413 - f1_score: 0.8615 - balanced_accuracy: 0.9034 - specificity: 0.9914 - miss_rate: 0.1845 - fall_out: 0.0086 - mcc: 0.8488
63/63 [==============================] - 1s 8ms/step - loss: 0.8545 - accuracy: 0.7466 - recall: 0.6810 - precision: 0.8086 - AUROC: 0.9562 - AUPRC: 0.8195 - f1_score: 0.7393 - balanced_accuracy: 0.8316 - specificity: 0.9821 - miss_rate: 0.3190 - fall_out: 0.0179 - mcc: 0.7163
6it [07:00, 66.75s/it]
-- HOLDOUT 7
Model: "CNN_MelS_3s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_36 (Conv2D) (None, 128, 130, 64) 640
max_pooling2d_27 (MaxPoolin (None, 64, 65, 64) 0
g2D)
conv2d_37 (Conv2D) (None, 64, 65, 64) 36928
max_pooling2d_28 (MaxPoolin (None, 32, 33, 64) 0
g2D)
conv2d_38 (Conv2D) (None, 32, 33, 128) 73856
max_pooling2d_29 (MaxPoolin (None, 16, 17, 128) 0
g2D)
conv2d_39 (Conv2D) (None, 16, 17, 128) 262272
flatten_9 (Flatten) (None, 34816) 0
dense_27 (Dense) (None, 128) 4456576
dropout_18 (Dropout) (None, 128) 0
dense_28 (Dense) (None, 128) 16512
dropout_19 (Dropout) (None, 128) 0
dense_29 (Dense) (None, 10) 1290
=================================================================
Total params: 4,848,074
Trainable params: 4,848,074
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 6s 74ms/step - loss: 2.8014 - accuracy: 0.1692 - recall: 0.0094 - precision: 0.1736 - AUROC: 0.5815 - AUPRC: 0.1353 - f1_score: 0.0178 - balanced_accuracy: 0.5022 - specificity: 0.9950 - miss_rate: 0.9906 - fall_out: 0.0050 - mcc: 0.0181 - val_loss: 2.0771 - val_accuracy: 0.2434 - val_recall: 0.0120 - val_precision: 0.6000 - val_AUROC: 0.6946 - val_AUPRC: 0.2468 - val_f1_score: 0.0236 - val_balanced_accuracy: 0.5056 - val_specificity: 0.9991 - val_miss_rate: 0.9880 - val_fall_out: 8.9022e-04 - val_mcc: 0.0747
Epoch 2/100
63/63 [==============================] - 4s 66ms/step - loss: 2.0557 - accuracy: 0.2434 - recall: 0.0525 - precision: 0.5564 - AUROC: 0.7114 - AUPRC: 0.2443 - f1_score: 0.0959 - balanced_accuracy: 0.5239 - specificity: 0.9954 - miss_rate: 0.9475 - fall_out: 0.0046 - mcc: 0.1485 - val_loss: 1.8783 - val_accuracy: 0.3160 - val_recall: 0.0916 - val_precision: 0.6332 - val_AUROC: 0.7850 - val_AUPRC: 0.3112 - val_f1_score: 0.1601 - val_balanced_accuracy: 0.5429 - val_specificity: 0.9941 - val_miss_rate: 0.9084 - val_fall_out: 0.0059 - val_mcc: 0.2154
Epoch 3/100
63/63 [==============================] - 4s 66ms/step - loss: 1.9114 - accuracy: 0.3075 - recall: 0.0887 - precision: 0.5751 - AUROC: 0.7732 - AUPRC: 0.3090 - f1_score: 0.1537 - balanced_accuracy: 0.5407 - specificity: 0.9927 - miss_rate: 0.9113 - fall_out: 0.0073 - mcc: 0.1982 - val_loss: 1.7231 - val_accuracy: 0.4111 - val_recall: 0.0911 - val_precision: 0.7647 - val_AUROC: 0.8355 - val_AUPRC: 0.4233 - val_f1_score: 0.1629 - val_balanced_accuracy: 0.5440 - val_specificity: 0.9969 - val_miss_rate: 0.9089 - val_fall_out: 0.0031 - val_mcc: 0.2433
Epoch 4/100
63/63 [==============================] - 4s 66ms/step - loss: 1.7175 - accuracy: 0.3944 - recall: 0.1462 - precision: 0.6455 - AUROC: 0.8258 - AUPRC: 0.4009 - f1_score: 0.2384 - balanced_accuracy: 0.5686 - specificity: 0.9911 - miss_rate: 0.8538 - fall_out: 0.0089 - mcc: 0.2768 - val_loss: 1.4244 - val_accuracy: 0.5168 - val_recall: 0.2098 - val_precision: 0.7921 - val_AUROC: 0.8891 - val_AUPRC: 0.5507 - val_f1_score: 0.3317 - val_balanced_accuracy: 0.6018 - val_specificity: 0.9939 - val_miss_rate: 0.7902 - val_fall_out: 0.0061 - val_mcc: 0.3805
Epoch 5/100
63/63 [==============================] - 4s 66ms/step - loss: 1.5267 - accuracy: 0.4714 - recall: 0.2348 - precision: 0.7081 - AUROC: 0.8660 - AUPRC: 0.4972 - f1_score: 0.3527 - balanced_accuracy: 0.6120 - specificity: 0.9892 - miss_rate: 0.7652 - fall_out: 0.0108 - mcc: 0.3754 - val_loss: 1.3274 - val_accuracy: 0.5483 - val_recall: 0.2809 - val_precision: 0.8095 - val_AUROC: 0.9003 - val_AUPRC: 0.5995 - val_f1_score: 0.4171 - val_balanced_accuracy: 0.6368 - val_specificity: 0.9927 - val_miss_rate: 0.7191 - val_fall_out: 0.0073 - val_mcc: 0.4484
Epoch 6/100
63/63 [==============================] - 4s 66ms/step - loss: 1.3735 - accuracy: 0.5262 - recall: 0.3116 - precision: 0.7363 - AUROC: 0.8938 - AUPRC: 0.5734 - f1_score: 0.4379 - balanced_accuracy: 0.6496 - specificity: 0.9876 - miss_rate: 0.6884 - fall_out: 0.0124 - mcc: 0.4459 - val_loss: 1.1403 - val_accuracy: 0.6124 - val_recall: 0.3966 - val_precision: 0.8311 - val_AUROC: 0.9273 - val_AUPRC: 0.6902 - val_f1_score: 0.5369 - val_balanced_accuracy: 0.6938 - val_specificity: 0.9910 - val_miss_rate: 0.6034 - val_fall_out: 0.0090 - val_mcc: 0.5455
Epoch 7/100
63/63 [==============================] - 4s 66ms/step - loss: 1.2989 - accuracy: 0.5630 - recall: 0.3556 - precision: 0.7448 - AUROC: 0.9050 - AUPRC: 0.6102 - f1_score: 0.4813 - balanced_accuracy: 0.6710 - specificity: 0.9865 - miss_rate: 0.6444 - fall_out: 0.0135 - mcc: 0.4812 - val_loss: 1.0605 - val_accuracy: 0.6380 - val_recall: 0.4316 - val_precision: 0.8241 - val_AUROC: 0.9376 - val_AUPRC: 0.7173 - val_f1_score: 0.5665 - val_balanced_accuracy: 0.7107 - val_specificity: 0.9898 - val_miss_rate: 0.5684 - val_fall_out: 0.0102 - val_mcc: 0.5675
Epoch 8/100
63/63 [==============================] - 4s 66ms/step - loss: 1.1602 - accuracy: 0.6137 - recall: 0.4257 - precision: 0.7819 - AUROC: 0.9238 - AUPRC: 0.6737 - f1_score: 0.5513 - balanced_accuracy: 0.7063 - specificity: 0.9868 - miss_rate: 0.5743 - fall_out: 0.0132 - mcc: 0.5454 - val_loss: 1.0422 - val_accuracy: 0.6545 - val_recall: 0.4522 - val_precision: 0.8300 - val_AUROC: 0.9394 - val_AUPRC: 0.7302 - val_f1_score: 0.5854 - val_balanced_accuracy: 0.7209 - val_specificity: 0.9897 - val_miss_rate: 0.5478 - val_fall_out: 0.0103 - val_mcc: 0.5841
Epoch 9/100
63/63 [==============================] - 4s 66ms/step - loss: 1.0836 - accuracy: 0.6437 - recall: 0.4692 - precision: 0.7931 - AUROC: 0.9334 - AUPRC: 0.7093 - f1_score: 0.5896 - balanced_accuracy: 0.7278 - specificity: 0.9864 - miss_rate: 0.5308 - fall_out: 0.0136 - mcc: 0.5793 - val_loss: 1.1124 - val_accuracy: 0.6380 - val_recall: 0.5378 - val_precision: 0.7448 - val_AUROC: 0.9311 - val_AUPRC: 0.7119 - val_f1_score: 0.6246 - val_balanced_accuracy: 0.7587 - val_specificity: 0.9795 - val_miss_rate: 0.4622 - val_fall_out: 0.0205 - val_mcc: 0.5996
Epoch 10/100
63/63 [==============================] - 4s 66ms/step - loss: 0.9827 - accuracy: 0.6740 - recall: 0.5329 - precision: 0.8119 - AUROC: 0.9449 - AUPRC: 0.7522 - f1_score: 0.6435 - balanced_accuracy: 0.7596 - specificity: 0.9863 - miss_rate: 0.4671 - fall_out: 0.0137 - mcc: 0.6290 - val_loss: 0.9707 - val_accuracy: 0.6945 - val_recall: 0.5413 - val_precision: 0.8380 - val_AUROC: 0.9474 - val_AUPRC: 0.7691 - val_f1_score: 0.6577 - val_balanced_accuracy: 0.7648 - val_specificity: 0.9884 - val_miss_rate: 0.4587 - val_fall_out: 0.0116 - val_mcc: 0.6464
Epoch 11/100
63/63 [==============================] - 4s 66ms/step - loss: 0.9440 - accuracy: 0.6921 - recall: 0.5483 - precision: 0.8212 - AUROC: 0.9489 - AUPRC: 0.7691 - f1_score: 0.6576 - balanced_accuracy: 0.7675 - specificity: 0.9867 - miss_rate: 0.4517 - fall_out: 0.0133 - mcc: 0.6431 - val_loss: 0.9745 - val_accuracy: 0.6730 - val_recall: 0.5533 - val_precision: 0.7996 - val_AUROC: 0.9447 - val_AUPRC: 0.7575 - val_f1_score: 0.6540 - val_balanced_accuracy: 0.7690 - val_specificity: 0.9846 - val_miss_rate: 0.4467 - val_fall_out: 0.0154 - val_mcc: 0.6358
Epoch 12/100
63/63 [==============================] - 4s 66ms/step - loss: 0.8489 - accuracy: 0.7213 - recall: 0.5989 - precision: 0.8357 - AUROC: 0.9585 - AUPRC: 0.8026 - f1_score: 0.6978 - balanced_accuracy: 0.7929 - specificity: 0.9869 - miss_rate: 0.4011 - fall_out: 0.0131 - mcc: 0.6814 - val_loss: 0.9019 - val_accuracy: 0.7111 - val_recall: 0.5884 - val_precision: 0.8363 - val_AUROC: 0.9536 - val_AUPRC: 0.7913 - val_f1_score: 0.6908 - val_balanced_accuracy: 0.7878 - val_specificity: 0.9872 - val_miss_rate: 0.4116 - val_fall_out: 0.0128 - val_mcc: 0.6752
Epoch 13/100
63/63 [==============================] - 4s 66ms/step - loss: 0.7664 - accuracy: 0.7461 - recall: 0.6373 - precision: 0.8566 - AUROC: 0.9654 - AUPRC: 0.8351 - f1_score: 0.7308 - balanced_accuracy: 0.8127 - specificity: 0.9881 - miss_rate: 0.3627 - fall_out: 0.0119 - mcc: 0.7150 - val_loss: 0.8185 - val_accuracy: 0.7301 - val_recall: 0.6274 - val_precision: 0.8320 - val_AUROC: 0.9602 - val_AUPRC: 0.8182 - val_f1_score: 0.7154 - val_balanced_accuracy: 0.8067 - val_specificity: 0.9859 - val_miss_rate: 0.3726 - val_fall_out: 0.0141 - val_mcc: 0.6969
Epoch 14/100
63/63 [==============================] - 4s 66ms/step - loss: 0.7177 - accuracy: 0.7667 - recall: 0.6656 - precision: 0.8597 - AUROC: 0.9695 - AUPRC: 0.8509 - f1_score: 0.7503 - balanced_accuracy: 0.8268 - specificity: 0.9879 - miss_rate: 0.3344 - fall_out: 0.0121 - mcc: 0.7336 - val_loss: 0.8391 - val_accuracy: 0.7351 - val_recall: 0.6450 - val_precision: 0.8209 - val_AUROC: 0.9574 - val_AUPRC: 0.8193 - val_f1_score: 0.7224 - val_balanced_accuracy: 0.8147 - val_specificity: 0.9844 - val_miss_rate: 0.3550 - val_fall_out: 0.0156 - val_mcc: 0.7017
Epoch 15/100
63/63 [==============================] - 4s 66ms/step - loss: 0.6332 - accuracy: 0.7910 - recall: 0.7093 - precision: 0.8730 - AUROC: 0.9757 - AUPRC: 0.8792 - f1_score: 0.7827 - balanced_accuracy: 0.8489 - specificity: 0.9885 - miss_rate: 0.2907 - fall_out: 0.0115 - mcc: 0.7662 - val_loss: 0.8518 - val_accuracy: 0.7466 - val_recall: 0.6615 - val_precision: 0.8236 - val_AUROC: 0.9579 - val_AUPRC: 0.8226 - val_f1_score: 0.7337 - val_balanced_accuracy: 0.8229 - val_specificity: 0.9843 - val_miss_rate: 0.3385 - val_fall_out: 0.0157 - val_mcc: 0.7128
250/250 [==============================] - 2s 9ms/step - loss: 0.3554 - accuracy: 0.8903 - recall: 0.8258 - precision: 0.9500 - AUROC: 0.9936 - AUPRC: 0.9608 - f1_score: 0.8835 - balanced_accuracy: 0.9105 - specificity: 0.9952 - miss_rate: 0.1742 - fall_out: 0.0048 - mcc: 0.8742
63/63 [==============================] - 1s 9ms/step - loss: 0.8518 - accuracy: 0.7466 - recall: 0.6615 - precision: 0.8236 - AUROC: 0.9579 - AUPRC: 0.8226 - f1_score: 0.7337 - balanced_accuracy: 0.8229 - specificity: 0.9843 - miss_rate: 0.3385 - fall_out: 0.0157 - mcc: 0.7128
7it [08:07, 66.90s/it]
-- HOLDOUT 8
Model: "CNN_MelS_3s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_40 (Conv2D) (None, 128, 130, 64) 640
max_pooling2d_30 (MaxPoolin (None, 64, 65, 64) 0
g2D)
conv2d_41 (Conv2D) (None, 64, 65, 64) 36928
max_pooling2d_31 (MaxPoolin (None, 32, 33, 64) 0
g2D)
conv2d_42 (Conv2D) (None, 32, 33, 128) 73856
max_pooling2d_32 (MaxPoolin (None, 16, 17, 128) 0
g2D)
conv2d_43 (Conv2D) (None, 16, 17, 128) 262272
flatten_10 (Flatten) (None, 34816) 0
dense_30 (Dense) (None, 128) 4456576
dropout_20 (Dropout) (None, 128) 0
dense_31 (Dense) (None, 128) 16512
dropout_21 (Dropout) (None, 128) 0
dense_32 (Dense) (None, 10) 1290
=================================================================
Total params: 4,848,074
Trainable params: 4,848,074
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 6s 74ms/step - loss: 2.9507 - accuracy: 0.1472 - recall: 0.0090 - precision: 0.1782 - AUROC: 0.5801 - AUPRC: 0.1331 - f1_score: 0.0172 - balanced_accuracy: 0.5022 - specificity: 0.9954 - miss_rate: 0.9910 - fall_out: 0.0046 - mcc: 0.0186 - val_loss: 2.1070 - val_accuracy: 0.2253 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_AUROC: 0.7277 - val_AUPRC: 0.2596 - val_f1_score: 0.0000e+00 - val_balanced_accuracy: 0.5000 - val_specificity: 1.0000 - val_miss_rate: 1.0000 - val_fall_out: 0.0000e+00 - val_mcc: 0.0000e+00
Epoch 2/100
63/63 [==============================] - 4s 68ms/step - loss: 2.0936 - accuracy: 0.2166 - recall: 0.0427 - precision: 0.6004 - AUROC: 0.6923 - AUPRC: 0.2302 - f1_score: 0.0797 - balanced_accuracy: 0.5198 - specificity: 0.9968 - miss_rate: 0.9573 - fall_out: 0.0032 - mcc: 0.1412 - val_loss: 1.9507 - val_accuracy: 0.2769 - val_recall: 0.0611 - val_precision: 0.7673 - val_AUROC: 0.7857 - val_AUPRC: 0.3265 - val_f1_score: 0.1132 - val_balanced_accuracy: 0.5295 - val_specificity: 0.9979 - val_miss_rate: 0.9389 - val_fall_out: 0.0021 - val_mcc: 0.1993
Epoch 3/100
63/63 [==============================] - 4s 66ms/step - loss: 1.9786 - accuracy: 0.2627 - recall: 0.0634 - precision: 0.5610 - AUROC: 0.7490 - AUPRC: 0.2712 - f1_score: 0.1139 - balanced_accuracy: 0.5289 - specificity: 0.9945 - miss_rate: 0.9366 - fall_out: 0.0055 - mcc: 0.1643 - val_loss: 1.7672 - val_accuracy: 0.3495 - val_recall: 0.0791 - val_precision: 0.7783 - val_AUROC: 0.8256 - val_AUPRC: 0.3878 - val_f1_score: 0.1436 - val_balanced_accuracy: 0.5383 - val_specificity: 0.9975 - val_miss_rate: 0.9209 - val_fall_out: 0.0025 - val_mcc: 0.2291
Epoch 4/100
63/63 [==============================] - 4s 71ms/step - loss: 1.8640 - accuracy: 0.3017 - recall: 0.0894 - precision: 0.5829 - AUROC: 0.7913 - AUPRC: 0.3151 - f1_score: 0.1551 - balanced_accuracy: 0.5412 - specificity: 0.9929 - miss_rate: 0.9106 - fall_out: 0.0071 - mcc: 0.2009 - val_loss: 1.6765 - val_accuracy: 0.3971 - val_recall: 0.1137 - val_precision: 0.7592 - val_AUROC: 0.8398 - val_AUPRC: 0.4100 - val_f1_score: 0.1977 - val_balanced_accuracy: 0.5548 - val_specificity: 0.9960 - val_miss_rate: 0.8863 - val_fall_out: 0.0040 - val_mcc: 0.2709
Epoch 5/100
63/63 [==============================] - 4s 69ms/step - loss: 1.7841 - accuracy: 0.3396 - recall: 0.1033 - precision: 0.5769 - AUROC: 0.8122 - AUPRC: 0.3425 - f1_score: 0.1753 - balanced_accuracy: 0.5475 - specificity: 0.9916 - miss_rate: 0.8967 - fall_out: 0.0084 - mcc: 0.2147 - val_loss: 1.5583 - val_accuracy: 0.4542 - val_recall: 0.1242 - val_precision: 0.7403 - val_AUROC: 0.8678 - val_AUPRC: 0.4563 - val_f1_score: 0.2127 - val_balanced_accuracy: 0.5597 - val_specificity: 0.9952 - val_miss_rate: 0.8758 - val_fall_out: 0.0048 - val_mcc: 0.2788
Epoch 6/100
63/63 [==============================] - 4s 66ms/step - loss: 1.6586 - accuracy: 0.3853 - recall: 0.1370 - precision: 0.6170 - AUROC: 0.8421 - AUPRC: 0.3977 - f1_score: 0.2242 - balanced_accuracy: 0.5638 - specificity: 0.9906 - miss_rate: 0.8630 - fall_out: 0.0094 - mcc: 0.2597 - val_loss: 1.3972 - val_accuracy: 0.5043 - val_recall: 0.2248 - val_precision: 0.7458 - val_AUROC: 0.8917 - val_AUPRC: 0.5396 - val_f1_score: 0.3455 - val_balanced_accuracy: 0.6082 - val_specificity: 0.9915 - val_miss_rate: 0.7752 - val_fall_out: 0.0085 - val_mcc: 0.3795
Epoch 7/100
63/63 [==============================] - 4s 66ms/step - loss: 1.5480 - accuracy: 0.4490 - recall: 0.1986 - precision: 0.6706 - AUROC: 0.8631 - AUPRC: 0.4617 - f1_score: 0.3065 - balanced_accuracy: 0.5939 - specificity: 0.9892 - miss_rate: 0.8014 - fall_out: 0.0108 - mcc: 0.3323 - val_loss: 1.3782 - val_accuracy: 0.4967 - val_recall: 0.2273 - val_precision: 0.7787 - val_AUROC: 0.8963 - val_AUPRC: 0.5570 - val_f1_score: 0.3519 - val_balanced_accuracy: 0.6101 - val_specificity: 0.9928 - val_miss_rate: 0.7727 - val_fall_out: 0.0072 - val_mcc: 0.3923
Epoch 8/100
63/63 [==============================] - 4s 66ms/step - loss: 1.4254 - accuracy: 0.4991 - recall: 0.2584 - precision: 0.7114 - AUROC: 0.8860 - AUPRC: 0.5253 - f1_score: 0.3791 - balanced_accuracy: 0.6234 - specificity: 0.9884 - miss_rate: 0.7416 - fall_out: 0.0116 - mcc: 0.3957 - val_loss: 1.2433 - val_accuracy: 0.5694 - val_recall: 0.3045 - val_precision: 0.7815 - val_AUROC: 0.9159 - val_AUPRC: 0.6221 - val_f1_score: 0.4382 - val_balanced_accuracy: 0.6475 - val_specificity: 0.9905 - val_miss_rate: 0.6955 - val_fall_out: 0.0095 - val_mcc: 0.4574
Epoch 9/100
63/63 [==============================] - 4s 66ms/step - loss: 1.2997 - accuracy: 0.5516 - recall: 0.3335 - precision: 0.7441 - AUROC: 0.9054 - AUPRC: 0.5979 - f1_score: 0.4606 - balanced_accuracy: 0.6604 - specificity: 0.9873 - miss_rate: 0.6665 - fall_out: 0.0127 - mcc: 0.4651 - val_loss: 1.1283 - val_accuracy: 0.6345 - val_recall: 0.3691 - val_precision: 0.8580 - val_AUROC: 0.9315 - val_AUPRC: 0.6983 - val_f1_score: 0.5161 - val_balanced_accuracy: 0.6811 - val_specificity: 0.9932 - val_miss_rate: 0.6309 - val_fall_out: 0.0068 - val_mcc: 0.5357
Epoch 10/100
63/63 [==============================] - 4s 66ms/step - loss: 1.2479 - accuracy: 0.5716 - recall: 0.3601 - precision: 0.7556 - AUROC: 0.9136 - AUPRC: 0.6254 - f1_score: 0.4877 - balanced_accuracy: 0.6736 - specificity: 0.9871 - miss_rate: 0.6399 - fall_out: 0.0129 - mcc: 0.4889 - val_loss: 1.0231 - val_accuracy: 0.6580 - val_recall: 0.4562 - val_precision: 0.8141 - val_AUROC: 0.9418 - val_AUPRC: 0.7283 - val_f1_score: 0.5847 - val_balanced_accuracy: 0.7223 - val_specificity: 0.9884 - val_miss_rate: 0.5438 - val_fall_out: 0.0116 - val_mcc: 0.5800
Epoch 11/100
63/63 [==============================] - 4s 66ms/step - loss: 1.1006 - accuracy: 0.6258 - recall: 0.4430 - precision: 0.7843 - AUROC: 0.9321 - AUPRC: 0.6930 - f1_score: 0.5662 - balanced_accuracy: 0.7147 - specificity: 0.9865 - miss_rate: 0.5570 - fall_out: 0.0135 - mcc: 0.5581 - val_loss: 0.9502 - val_accuracy: 0.6740 - val_recall: 0.5018 - val_precision: 0.8528 - val_AUROC: 0.9495 - val_AUPRC: 0.7671 - val_f1_score: 0.6318 - val_balanced_accuracy: 0.7461 - val_specificity: 0.9904 - val_miss_rate: 0.4982 - val_fall_out: 0.0096 - val_mcc: 0.6274
Epoch 12/100
63/63 [==============================] - 4s 66ms/step - loss: 1.0242 - accuracy: 0.6622 - recall: 0.4828 - precision: 0.8050 - AUROC: 0.9411 - AUPRC: 0.7306 - f1_score: 0.6036 - balanced_accuracy: 0.7349 - specificity: 0.9870 - miss_rate: 0.5172 - fall_out: 0.0130 - mcc: 0.5936 - val_loss: 0.9752 - val_accuracy: 0.6875 - val_recall: 0.4922 - val_precision: 0.8570 - val_AUROC: 0.9463 - val_AUPRC: 0.7624 - val_f1_score: 0.6253 - val_balanced_accuracy: 0.7416 - val_specificity: 0.9909 - val_miss_rate: 0.5078 - val_fall_out: 0.0091 - val_mcc: 0.6229
Epoch 13/100
63/63 [==============================] - 4s 66ms/step - loss: 0.9667 - accuracy: 0.6782 - recall: 0.5248 - precision: 0.8109 - AUROC: 0.9478 - AUPRC: 0.7548 - f1_score: 0.6372 - balanced_accuracy: 0.7556 - specificity: 0.9864 - miss_rate: 0.4752 - fall_out: 0.0136 - mcc: 0.6234 - val_loss: 0.9054 - val_accuracy: 0.7021 - val_recall: 0.5613 - val_precision: 0.8366 - val_AUROC: 0.9519 - val_AUPRC: 0.7832 - val_f1_score: 0.6719 - val_balanced_accuracy: 0.7746 - val_specificity: 0.9878 - val_miss_rate: 0.4387 - val_fall_out: 0.0122 - val_mcc: 0.6585
Epoch 14/100
63/63 [==============================] - 4s 66ms/step - loss: 0.8592 - accuracy: 0.7095 - recall: 0.5844 - precision: 0.8331 - AUROC: 0.9575 - AUPRC: 0.7990 - f1_score: 0.6869 - balanced_accuracy: 0.7857 - specificity: 0.9870 - miss_rate: 0.4156 - fall_out: 0.0130 - mcc: 0.6712 - val_loss: 0.8806 - val_accuracy: 0.7101 - val_recall: 0.5694 - val_precision: 0.8472 - val_AUROC: 0.9555 - val_AUPRC: 0.7901 - val_f1_score: 0.6810 - val_balanced_accuracy: 0.7790 - val_specificity: 0.9886 - val_miss_rate: 0.4306 - val_fall_out: 0.0114 - val_mcc: 0.6685
Epoch 15/100
63/63 [==============================] - 4s 66ms/step - loss: 0.8158 - accuracy: 0.7305 - recall: 0.5966 - precision: 0.8475 - AUROC: 0.9618 - AUPRC: 0.8146 - f1_score: 0.7002 - balanced_accuracy: 0.7923 - specificity: 0.9881 - miss_rate: 0.4034 - fall_out: 0.0119 - mcc: 0.6857 - val_loss: 0.8191 - val_accuracy: 0.7316 - val_recall: 0.6375 - val_precision: 0.8353 - val_AUROC: 0.9602 - val_AUPRC: 0.8196 - val_f1_score: 0.7231 - val_balanced_accuracy: 0.8117 - val_specificity: 0.9860 - val_miss_rate: 0.3625 - val_fall_out: 0.0140 - val_mcc: 0.7045
Epoch 16/100
63/63 [==============================] - 4s 66ms/step - loss: 0.7563 - accuracy: 0.7524 - recall: 0.6371 - precision: 0.8522 - AUROC: 0.9670 - AUPRC: 0.8376 - f1_score: 0.7292 - balanced_accuracy: 0.8124 - specificity: 0.9877 - miss_rate: 0.3629 - fall_out: 0.0123 - mcc: 0.7128 - val_loss: 0.8652 - val_accuracy: 0.7276 - val_recall: 0.6545 - val_precision: 0.8283 - val_AUROC: 0.9550 - val_AUPRC: 0.8064 - val_f1_score: 0.7312 - val_balanced_accuracy: 0.8197 - val_specificity: 0.9849 - val_miss_rate: 0.3455 - val_fall_out: 0.0151 - val_mcc: 0.7111
Epoch 17/100
63/63 [==============================] - 4s 66ms/step - loss: 0.7187 - accuracy: 0.7606 - recall: 0.6628 - precision: 0.8608 - AUROC: 0.9691 - AUPRC: 0.8497 - f1_score: 0.7489 - balanced_accuracy: 0.8255 - specificity: 0.9881 - miss_rate: 0.3372 - fall_out: 0.0119 - mcc: 0.7325 - val_loss: 0.9146 - val_accuracy: 0.7171 - val_recall: 0.6269 - val_precision: 0.7969 - val_AUROC: 0.9523 - val_AUPRC: 0.7904 - val_f1_score: 0.7018 - val_balanced_accuracy: 0.8046 - val_specificity: 0.9823 - val_miss_rate: 0.3731 - val_fall_out: 0.0177 - val_mcc: 0.6788
250/250 [==============================] - 2s 8ms/step - loss: 0.4799 - accuracy: 0.8456 - recall: 0.7573 - precision: 0.9208 - AUROC: 0.9874 - AUPRC: 0.9274 - f1_score: 0.8311 - balanced_accuracy: 0.8750 - specificity: 0.9928 - miss_rate: 0.2427 - fall_out: 0.0072 - mcc: 0.8190
63/63 [==============================] - 1s 8ms/step - loss: 0.9146 - accuracy: 0.7171 - recall: 0.6269 - precision: 0.7969 - AUROC: 0.9523 - AUPRC: 0.7904 - f1_score: 0.7018 - balanced_accuracy: 0.8046 - specificity: 0.9823 - miss_rate: 0.3731 - fall_out: 0.0177 - mcc: 0.6788
8it [09:23, 69.88s/it]
-- HOLDOUT 9
Model: "CNN_MelS_3s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_44 (Conv2D) (None, 128, 130, 64) 640
max_pooling2d_33 (MaxPoolin (None, 64, 65, 64) 0
g2D)
conv2d_45 (Conv2D) (None, 64, 65, 64) 36928
max_pooling2d_34 (MaxPoolin (None, 32, 33, 64) 0
g2D)
conv2d_46 (Conv2D) (None, 32, 33, 128) 73856
max_pooling2d_35 (MaxPoolin (None, 16, 17, 128) 0
g2D)
conv2d_47 (Conv2D) (None, 16, 17, 128) 262272
flatten_11 (Flatten) (None, 34816) 0
dense_33 (Dense) (None, 128) 4456576
dropout_22 (Dropout) (None, 128) 0
dense_34 (Dense) (None, 128) 16512
dropout_23 (Dropout) (None, 128) 0
dense_35 (Dense) (None, 10) 1290
=================================================================
Total params: 4,848,074
Trainable params: 4,848,074
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 6s 75ms/step - loss: 2.8391 - accuracy: 0.1609 - recall: 0.0143 - precision: 0.2235 - AUROC: 0.6052 - AUPRC: 0.1489 - f1_score: 0.0268 - balanced_accuracy: 0.5044 - specificity: 0.9945 - miss_rate: 0.9857 - fall_out: 0.0055 - mcc: 0.0330 - val_loss: 2.0300 - val_accuracy: 0.2384 - val_recall: 0.0155 - val_precision: 0.8378 - val_AUROC: 0.7362 - val_AUPRC: 0.2920 - val_f1_score: 0.0305 - val_balanced_accuracy: 0.5076 - val_specificity: 0.9997 - val_miss_rate: 0.9845 - val_fall_out: 3.3383e-04 - val_mcc: 0.1060
Epoch 2/100
63/63 [==============================] - 4s 66ms/step - loss: 2.0236 - accuracy: 0.2460 - recall: 0.0567 - precision: 0.5213 - AUROC: 0.7347 - AUPRC: 0.2570 - f1_score: 0.1023 - balanced_accuracy: 0.5255 - specificity: 0.9942 - miss_rate: 0.9433 - fall_out: 0.0058 - mcc: 0.1473 - val_loss: 1.8512 - val_accuracy: 0.3515 - val_recall: 0.0841 - val_precision: 0.6462 - val_AUROC: 0.8142 - val_AUPRC: 0.3612 - val_f1_score: 0.1489 - val_balanced_accuracy: 0.5395 - val_specificity: 0.9949 - val_miss_rate: 0.9159 - val_fall_out: 0.0051 - val_mcc: 0.2091
Epoch 3/100
63/63 [==============================] - 4s 68ms/step - loss: 1.8969 - accuracy: 0.3059 - recall: 0.0844 - precision: 0.5712 - AUROC: 0.7802 - AUPRC: 0.3073 - f1_score: 0.1471 - balanced_accuracy: 0.5387 - specificity: 0.9930 - miss_rate: 0.9156 - fall_out: 0.0070 - mcc: 0.1924 - val_loss: 1.6775 - val_accuracy: 0.4196 - val_recall: 0.1122 - val_precision: 0.7226 - val_AUROC: 0.8495 - val_AUPRC: 0.4174 - val_f1_score: 0.1942 - val_balanced_accuracy: 0.5537 - val_specificity: 0.9952 - val_miss_rate: 0.8878 - val_fall_out: 0.0048 - val_mcc: 0.2606
Epoch 4/100
63/63 [==============================] - 4s 68ms/step - loss: 1.7475 - accuracy: 0.3666 - recall: 0.1318 - precision: 0.6254 - AUROC: 0.8202 - AUPRC: 0.3712 - f1_score: 0.2177 - balanced_accuracy: 0.5615 - specificity: 0.9912 - miss_rate: 0.8682 - fall_out: 0.0088 - mcc: 0.2569 - val_loss: 1.4992 - val_accuracy: 0.5003 - val_recall: 0.1627 - val_precision: 0.7812 - val_AUROC: 0.8818 - val_AUPRC: 0.5143 - val_f1_score: 0.2694 - val_balanced_accuracy: 0.5788 - val_specificity: 0.9949 - val_miss_rate: 0.8373 - val_fall_out: 0.0051 - val_mcc: 0.3312
Epoch 5/100
63/63 [==============================] - 4s 68ms/step - loss: 1.6272 - accuracy: 0.4103 - recall: 0.1727 - precision: 0.6381 - AUROC: 0.8475 - AUPRC: 0.4284 - f1_score: 0.2719 - balanced_accuracy: 0.5809 - specificity: 0.9891 - miss_rate: 0.8273 - fall_out: 0.0109 - mcc: 0.2992 - val_loss: 1.4120 - val_accuracy: 0.5143 - val_recall: 0.2599 - val_precision: 0.7522 - val_AUROC: 0.8864 - val_AUPRC: 0.5397 - val_f1_score: 0.3863 - val_balanced_accuracy: 0.6252 - val_specificity: 0.9905 - val_miss_rate: 0.7401 - val_fall_out: 0.0095 - val_mcc: 0.4113
Epoch 6/100
63/63 [==============================] - 4s 67ms/step - loss: 1.4904 - accuracy: 0.4718 - recall: 0.2396 - precision: 0.6995 - AUROC: 0.8734 - AUPRC: 0.5034 - f1_score: 0.3569 - balanced_accuracy: 0.6141 - specificity: 0.9886 - miss_rate: 0.7604 - fall_out: 0.0114 - mcc: 0.3763 - val_loss: 1.2757 - val_accuracy: 0.5448 - val_recall: 0.2954 - val_precision: 0.7554 - val_AUROC: 0.9116 - val_AUPRC: 0.6015 - val_f1_score: 0.4248 - val_balanced_accuracy: 0.6424 - val_specificity: 0.9894 - val_miss_rate: 0.7046 - val_fall_out: 0.0106 - val_mcc: 0.4408
Epoch 7/100
63/63 [==============================] - 4s 66ms/step - loss: 1.3794 - accuracy: 0.5109 - recall: 0.2878 - precision: 0.7115 - AUROC: 0.8932 - AUPRC: 0.5558 - f1_score: 0.4098 - balanced_accuracy: 0.6374 - specificity: 0.9870 - miss_rate: 0.7122 - fall_out: 0.0130 - mcc: 0.4185 - val_loss: 1.2096 - val_accuracy: 0.5834 - val_recall: 0.3290 - val_precision: 0.8182 - val_AUROC: 0.9219 - val_AUPRC: 0.6422 - val_f1_score: 0.4693 - val_balanced_accuracy: 0.6604 - val_specificity: 0.9919 - val_miss_rate: 0.6710 - val_fall_out: 0.0081 - val_mcc: 0.4900
Epoch 8/100
63/63 [==============================] - 4s 66ms/step - loss: 1.2893 - accuracy: 0.5470 - recall: 0.3372 - precision: 0.7555 - AUROC: 0.9061 - AUPRC: 0.6034 - f1_score: 0.4663 - balanced_accuracy: 0.6625 - specificity: 0.9879 - miss_rate: 0.6628 - fall_out: 0.0121 - mcc: 0.4723 - val_loss: 1.0740 - val_accuracy: 0.6279 - val_recall: 0.4006 - val_precision: 0.8395 - val_AUROC: 0.9380 - val_AUPRC: 0.7092 - val_f1_score: 0.5424 - val_balanced_accuracy: 0.6960 - val_specificity: 0.9915 - val_miss_rate: 0.5994 - val_fall_out: 0.0085 - val_mcc: 0.5518
Epoch 9/100
63/63 [==============================] - 4s 66ms/step - loss: 1.1632 - accuracy: 0.5987 - recall: 0.4026 - precision: 0.7720 - AUROC: 0.9244 - AUPRC: 0.6660 - f1_score: 0.5292 - balanced_accuracy: 0.6947 - specificity: 0.9868 - miss_rate: 0.5974 - fall_out: 0.0132 - mcc: 0.5254 - val_loss: 1.1495 - val_accuracy: 0.5999 - val_recall: 0.4136 - val_precision: 0.8066 - val_AUROC: 0.9262 - val_AUPRC: 0.6719 - val_f1_score: 0.5468 - val_balanced_accuracy: 0.7013 - val_specificity: 0.9890 - val_miss_rate: 0.5864 - val_fall_out: 0.0110 - val_mcc: 0.5476
Epoch 10/100
63/63 [==============================] - 4s 66ms/step - loss: 1.1411 - accuracy: 0.6066 - recall: 0.4175 - precision: 0.7749 - AUROC: 0.9267 - AUPRC: 0.6760 - f1_score: 0.5426 - balanced_accuracy: 0.7020 - specificity: 0.9865 - miss_rate: 0.5825 - fall_out: 0.0135 - mcc: 0.5368 - val_loss: 1.0127 - val_accuracy: 0.6510 - val_recall: 0.4442 - val_precision: 0.8360 - val_AUROC: 0.9432 - val_AUPRC: 0.7301 - val_f1_score: 0.5801 - val_balanced_accuracy: 0.7172 - val_specificity: 0.9903 - val_miss_rate: 0.5558 - val_fall_out: 0.0097 - val_mcc: 0.5811
Epoch 11/100
63/63 [==============================] - 4s 66ms/step - loss: 1.0618 - accuracy: 0.6318 - recall: 0.4653 - precision: 0.7941 - AUROC: 0.9365 - AUPRC: 0.7108 - f1_score: 0.5868 - balanced_accuracy: 0.7260 - specificity: 0.9866 - miss_rate: 0.5347 - fall_out: 0.0134 - mcc: 0.5772 - val_loss: 0.9501 - val_accuracy: 0.6740 - val_recall: 0.5163 - val_precision: 0.8315 - val_AUROC: 0.9490 - val_AUPRC: 0.7623 - val_f1_score: 0.6370 - val_balanced_accuracy: 0.7523 - val_specificity: 0.9884 - val_miss_rate: 0.4837 - val_fall_out: 0.0116 - val_mcc: 0.6273
Epoch 12/100
63/63 [==============================] - 4s 67ms/step - loss: 0.9993 - accuracy: 0.6607 - recall: 0.4994 - precision: 0.8011 - AUROC: 0.9440 - AUPRC: 0.7407 - f1_score: 0.6152 - balanced_accuracy: 0.7428 - specificity: 0.9862 - miss_rate: 0.5006 - fall_out: 0.0138 - mcc: 0.6026 - val_loss: 0.9920 - val_accuracy: 0.6725 - val_recall: 0.5468 - val_precision: 0.8149 - val_AUROC: 0.9428 - val_AUPRC: 0.7498 - val_f1_score: 0.6545 - val_balanced_accuracy: 0.7665 - val_specificity: 0.9862 - val_miss_rate: 0.4532 - val_fall_out: 0.0138 - val_mcc: 0.6391
Epoch 13/100
63/63 [==============================] - 4s 68ms/step - loss: 0.9324 - accuracy: 0.6884 - recall: 0.5428 - precision: 0.8159 - AUROC: 0.9502 - AUPRC: 0.7675 - f1_score: 0.6519 - balanced_accuracy: 0.7646 - specificity: 0.9864 - miss_rate: 0.4572 - fall_out: 0.0136 - mcc: 0.6371 - val_loss: 0.9194 - val_accuracy: 0.6915 - val_recall: 0.5588 - val_precision: 0.8254 - val_AUROC: 0.9521 - val_AUPRC: 0.7722 - val_f1_score: 0.6665 - val_balanced_accuracy: 0.7729 - val_specificity: 0.9869 - val_miss_rate: 0.4412 - val_fall_out: 0.0131 - val_mcc: 0.6516
Epoch 14/100
63/63 [==============================] - 4s 70ms/step - loss: 0.8322 - accuracy: 0.7229 - recall: 0.5992 - precision: 0.8372 - AUROC: 0.9601 - AUPRC: 0.8059 - f1_score: 0.6985 - balanced_accuracy: 0.7931 - specificity: 0.9871 - miss_rate: 0.4008 - fall_out: 0.0129 - mcc: 0.6823 - val_loss: 0.9776 - val_accuracy: 0.6720 - val_recall: 0.5388 - val_precision: 0.8258 - val_AUROC: 0.9460 - val_AUPRC: 0.7617 - val_f1_score: 0.6521 - val_balanced_accuracy: 0.7631 - val_specificity: 0.9874 - val_miss_rate: 0.4612 - val_fall_out: 0.0126 - val_mcc: 0.6392
Epoch 15/100
63/63 [==============================] - 4s 71ms/step - loss: 0.8333 - accuracy: 0.7300 - recall: 0.6016 - precision: 0.8400 - AUROC: 0.9599 - AUPRC: 0.8111 - f1_score: 0.7011 - balanced_accuracy: 0.7944 - specificity: 0.9873 - miss_rate: 0.3984 - fall_out: 0.0127 - mcc: 0.6851 - val_loss: 0.8747 - val_accuracy: 0.7141 - val_recall: 0.6154 - val_precision: 0.8248 - val_AUROC: 0.9570 - val_AUPRC: 0.7994 - val_f1_score: 0.7049 - val_balanced_accuracy: 0.8005 - val_specificity: 0.9855 - val_miss_rate: 0.3846 - val_fall_out: 0.0145 - val_mcc: 0.6861
Epoch 16/100
63/63 [==============================] - 4s 69ms/step - loss: 0.7786 - accuracy: 0.7422 - recall: 0.6364 - precision: 0.8456 - AUROC: 0.9652 - AUPRC: 0.8300 - f1_score: 0.7262 - balanced_accuracy: 0.8117 - specificity: 0.9871 - miss_rate: 0.3636 - fall_out: 0.0129 - mcc: 0.7090 - val_loss: 0.8892 - val_accuracy: 0.7186 - val_recall: 0.6114 - val_precision: 0.8173 - val_AUROC: 0.9553 - val_AUPRC: 0.7969 - val_f1_score: 0.6995 - val_balanced_accuracy: 0.7981 - val_specificity: 0.9848 - val_miss_rate: 0.3886 - val_fall_out: 0.0152 - val_mcc: 0.6799
Epoch 17/100
63/63 [==============================] - 4s 70ms/step - loss: 0.7481 - accuracy: 0.7570 - recall: 0.6566 - precision: 0.8551 - AUROC: 0.9677 - AUPRC: 0.8412 - f1_score: 0.7428 - balanced_accuracy: 0.8221 - specificity: 0.9876 - miss_rate: 0.3434 - fall_out: 0.0124 - mcc: 0.7259 - val_loss: 0.9897 - val_accuracy: 0.6915 - val_recall: 0.6199 - val_precision: 0.7870 - val_AUROC: 0.9453 - val_AUPRC: 0.7733 - val_f1_score: 0.6936 - val_balanced_accuracy: 0.8006 - val_specificity: 0.9814 - val_miss_rate: 0.3801 - val_fall_out: 0.0186 - val_mcc: 0.6696
250/250 [==============================] - 2s 9ms/step - loss: 0.5629 - accuracy: 0.8178 - recall: 0.7367 - precision: 0.8908 - AUROC: 0.9809 - AUPRC: 0.9008 - f1_score: 0.8065 - balanced_accuracy: 0.8633 - specificity: 0.9900 - miss_rate: 0.2633 - fall_out: 0.0100 - mcc: 0.7915
63/63 [==============================] - 1s 9ms/step - loss: 0.9897 - accuracy: 0.6915 - recall: 0.6199 - precision: 0.7865 - AUROC: 0.9453 - AUPRC: 0.7733 - f1_score: 0.6934 - balanced_accuracy: 0.8006 - specificity: 0.9813 - miss_rate: 0.3801 - fall_out: 0.0187 - mcc: 0.6694
9it [10:41, 72.47s/it]
-- HOLDOUT 10
Model: "CNN_MelS_3s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_48 (Conv2D) (None, 128, 130, 64) 640
max_pooling2d_36 (MaxPoolin (None, 64, 65, 64) 0
g2D)
conv2d_49 (Conv2D) (None, 64, 65, 64) 36928
max_pooling2d_37 (MaxPoolin (None, 32, 33, 64) 0
g2D)
conv2d_50 (Conv2D) (None, 32, 33, 128) 73856
max_pooling2d_38 (MaxPoolin (None, 16, 17, 128) 0
g2D)
conv2d_51 (Conv2D) (None, 16, 17, 128) 262272
flatten_12 (Flatten) (None, 34816) 0
dense_36 (Dense) (None, 128) 4456576
dropout_24 (Dropout) (None, 128) 0
dense_37 (Dense) (None, 128) 16512
dropout_25 (Dropout) (None, 128) 0
dense_38 (Dense) (None, 10) 1290
=================================================================
Total params: 4,848,074
Trainable params: 4,848,074
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 6s 77ms/step - loss: 2.8008 - accuracy: 0.1614 - recall: 0.0095 - precision: 0.1805 - AUROC: 0.5748 - AUPRC: 0.1306 - f1_score: 0.0181 - balanced_accuracy: 0.5024 - specificity: 0.9952 - miss_rate: 0.9905 - fall_out: 0.0048 - mcc: 0.0195 - val_loss: 2.1427 - val_accuracy: 0.2308 - val_recall: 0.0045 - val_precision: 0.6429 - val_AUROC: 0.7044 - val_AUPRC: 0.2209 - val_f1_score: 0.0090 - val_balanced_accuracy: 0.5021 - val_specificity: 0.9997 - val_miss_rate: 0.9955 - val_fall_out: 2.7820e-04 - val_mcc: 0.0479
Epoch 2/100
63/63 [==============================] - 4s 71ms/step - loss: 2.0764 - accuracy: 0.2327 - recall: 0.0407 - precision: 0.5546 - AUROC: 0.7022 - AUPRC: 0.2326 - f1_score: 0.0758 - balanced_accuracy: 0.5185 - specificity: 0.9964 - miss_rate: 0.9593 - fall_out: 0.0036 - mcc: 0.1303 - val_loss: 1.8402 - val_accuracy: 0.3605 - val_recall: 0.0746 - val_precision: 0.7487 - val_AUROC: 0.8095 - val_AUPRC: 0.3616 - val_f1_score: 0.1357 - val_balanced_accuracy: 0.5359 - val_specificity: 0.9972 - val_miss_rate: 0.9254 - val_fall_out: 0.0028 - val_mcc: 0.2170
Epoch 3/100
63/63 [==============================] - 4s 69ms/step - loss: 1.8920 - accuracy: 0.3139 - recall: 0.0860 - precision: 0.5788 - AUROC: 0.7772 - AUPRC: 0.3085 - f1_score: 0.1498 - balanced_accuracy: 0.5395 - specificity: 0.9930 - miss_rate: 0.9140 - fall_out: 0.0070 - mcc: 0.1961 - val_loss: 1.5966 - val_accuracy: 0.4231 - val_recall: 0.1442 - val_precision: 0.7094 - val_AUROC: 0.8636 - val_AUPRC: 0.4661 - val_f1_score: 0.2397 - val_balanced_accuracy: 0.5688 - val_specificity: 0.9934 - val_miss_rate: 0.8558 - val_fall_out: 0.0066 - val_mcc: 0.2926
Epoch 4/100
63/63 [==============================] - 5s 72ms/step - loss: 1.6854 - accuracy: 0.3990 - recall: 0.1632 - precision: 0.6409 - AUROC: 0.8331 - AUPRC: 0.4098 - f1_score: 0.2602 - balanced_accuracy: 0.5765 - specificity: 0.9898 - miss_rate: 0.8368 - fall_out: 0.0102 - mcc: 0.2915 - val_loss: 1.4322 - val_accuracy: 0.5053 - val_recall: 0.2494 - val_precision: 0.7806 - val_AUROC: 0.8905 - val_AUPRC: 0.5628 - val_f1_score: 0.3780 - val_balanced_accuracy: 0.6208 - val_specificity: 0.9922 - val_miss_rate: 0.7506 - val_fall_out: 0.0078 - val_mcc: 0.4121
Epoch 5/100
63/63 [==============================] - 4s 69ms/step - loss: 1.5121 - accuracy: 0.4662 - recall: 0.2426 - precision: 0.6940 - AUROC: 0.8695 - AUPRC: 0.4987 - f1_score: 0.3595 - balanced_accuracy: 0.6154 - specificity: 0.9881 - miss_rate: 0.7574 - fall_out: 0.0119 - mcc: 0.3769 - val_loss: 1.2947 - val_accuracy: 0.5699 - val_recall: 0.2609 - val_precision: 0.8626 - val_AUROC: 0.9151 - val_AUPRC: 0.6413 - val_f1_score: 0.4006 - val_balanced_accuracy: 0.6281 - val_specificity: 0.9954 - val_miss_rate: 0.7391 - val_fall_out: 0.0046 - val_mcc: 0.4489
Epoch 6/100
63/63 [==============================] - 4s 68ms/step - loss: 1.3906 - accuracy: 0.5209 - recall: 0.2906 - precision: 0.7367 - AUROC: 0.8911 - AUPRC: 0.5659 - f1_score: 0.4168 - balanced_accuracy: 0.6395 - specificity: 0.9885 - miss_rate: 0.7094 - fall_out: 0.0115 - mcc: 0.4301 - val_loss: 1.2425 - val_accuracy: 0.5799 - val_recall: 0.3070 - val_precision: 0.8646 - val_AUROC: 0.9177 - val_AUPRC: 0.6552 - val_f1_score: 0.4531 - val_balanced_accuracy: 0.6508 - val_specificity: 0.9947 - val_miss_rate: 0.6930 - val_fall_out: 0.0053 - val_mcc: 0.4890
Epoch 7/100
63/63 [==============================] - 4s 66ms/step - loss: 1.2793 - accuracy: 0.5566 - recall: 0.3583 - precision: 0.7555 - AUROC: 0.9082 - AUPRC: 0.6196 - f1_score: 0.4861 - balanced_accuracy: 0.6727 - specificity: 0.9871 - miss_rate: 0.6417 - fall_out: 0.0129 - mcc: 0.4876 - val_loss: 1.0892 - val_accuracy: 0.6430 - val_recall: 0.4131 - val_precision: 0.8532 - val_AUROC: 0.9348 - val_AUPRC: 0.7193 - val_f1_score: 0.5567 - val_balanced_accuracy: 0.7026 - val_specificity: 0.9921 - val_miss_rate: 0.5869 - val_fall_out: 0.0079 - val_mcc: 0.5663
Epoch 8/100
63/63 [==============================] - 4s 68ms/step - loss: 1.1537 - accuracy: 0.6103 - recall: 0.4168 - precision: 0.7958 - AUROC: 0.9260 - AUPRC: 0.6796 - f1_score: 0.5471 - balanced_accuracy: 0.7025 - specificity: 0.9881 - miss_rate: 0.5832 - fall_out: 0.0119 - mcc: 0.5453 - val_loss: 0.9794 - val_accuracy: 0.6710 - val_recall: 0.4962 - val_precision: 0.8420 - val_AUROC: 0.9460 - val_AUPRC: 0.7563 - val_f1_score: 0.6244 - val_balanced_accuracy: 0.7429 - val_specificity: 0.9897 - val_miss_rate: 0.5038 - val_fall_out: 0.0103 - val_mcc: 0.6190
Epoch 9/100
63/63 [==============================] - 4s 68ms/step - loss: 1.0425 - accuracy: 0.6536 - recall: 0.4860 - precision: 0.8051 - AUROC: 0.9386 - AUPRC: 0.7295 - f1_score: 0.6061 - balanced_accuracy: 0.7365 - specificity: 0.9869 - miss_rate: 0.5140 - fall_out: 0.0131 - mcc: 0.5957 - val_loss: 0.9937 - val_accuracy: 0.6775 - val_recall: 0.4822 - val_precision: 0.8507 - val_AUROC: 0.9472 - val_AUPRC: 0.7636 - val_f1_score: 0.6155 - val_balanced_accuracy: 0.7364 - val_specificity: 0.9906 - val_miss_rate: 0.5178 - val_fall_out: 0.0094 - val_mcc: 0.6134
Epoch 10/100
63/63 [==============================] - 4s 66ms/step - loss: 0.9774 - accuracy: 0.6791 - recall: 0.5283 - precision: 0.8222 - AUROC: 0.9454 - AUPRC: 0.7541 - f1_score: 0.6433 - balanced_accuracy: 0.7578 - specificity: 0.9873 - miss_rate: 0.4717 - fall_out: 0.0127 - mcc: 0.6308 - val_loss: 0.9385 - val_accuracy: 0.6895 - val_recall: 0.5528 - val_precision: 0.8473 - val_AUROC: 0.9499 - val_AUPRC: 0.7772 - val_f1_score: 0.6691 - val_balanced_accuracy: 0.7709 - val_specificity: 0.9889 - val_miss_rate: 0.4472 - val_fall_out: 0.0111 - val_mcc: 0.6581
Epoch 11/100
63/63 [==============================] - 4s 66ms/step - loss: 0.8909 - accuracy: 0.7097 - recall: 0.5731 - precision: 0.8306 - AUROC: 0.9536 - AUPRC: 0.7911 - f1_score: 0.6783 - balanced_accuracy: 0.7801 - specificity: 0.9870 - miss_rate: 0.4269 - fall_out: 0.0130 - mcc: 0.6630 - val_loss: 0.9472 - val_accuracy: 0.6950 - val_recall: 0.5789 - val_precision: 0.8281 - val_AUROC: 0.9478 - val_AUPRC: 0.7800 - val_f1_score: 0.6814 - val_balanced_accuracy: 0.7828 - val_specificity: 0.9866 - val_miss_rate: 0.4211 - val_fall_out: 0.0134 - val_mcc: 0.6653
Epoch 12/100
63/63 [==============================] - 4s 66ms/step - loss: 0.8591 - accuracy: 0.7261 - recall: 0.5952 - precision: 0.8426 - AUROC: 0.9571 - AUPRC: 0.8055 - f1_score: 0.6976 - balanced_accuracy: 0.7914 - specificity: 0.9876 - miss_rate: 0.4048 - fall_out: 0.0124 - mcc: 0.6824 - val_loss: 0.8428 - val_accuracy: 0.7261 - val_recall: 0.6204 - val_precision: 0.8349 - val_AUROC: 0.9588 - val_AUPRC: 0.8117 - val_f1_score: 0.7119 - val_balanced_accuracy: 0.8034 - val_specificity: 0.9864 - val_miss_rate: 0.3796 - val_fall_out: 0.0136 - val_mcc: 0.6941
Epoch 13/100
63/63 [==============================] - 4s 66ms/step - loss: 0.7581 - accuracy: 0.7560 - recall: 0.6518 - precision: 0.8616 - AUROC: 0.9660 - AUPRC: 0.8389 - f1_score: 0.7422 - balanced_accuracy: 0.8201 - specificity: 0.9884 - miss_rate: 0.3482 - fall_out: 0.0116 - mcc: 0.7263 - val_loss: 0.7880 - val_accuracy: 0.7436 - val_recall: 0.6670 - val_precision: 0.8335 - val_AUROC: 0.9622 - val_AUPRC: 0.8295 - val_f1_score: 0.7410 - val_balanced_accuracy: 0.8261 - val_specificity: 0.9852 - val_miss_rate: 0.3330 - val_fall_out: 0.0148 - val_mcc: 0.7211
Epoch 14/100
63/63 [==============================] - 4s 66ms/step - loss: 0.7174 - accuracy: 0.7690 - recall: 0.6691 - precision: 0.8640 - AUROC: 0.9694 - AUPRC: 0.8534 - f1_score: 0.7541 - balanced_accuracy: 0.8287 - specificity: 0.9883 - miss_rate: 0.3309 - fall_out: 0.0117 - mcc: 0.7378 - val_loss: 0.8237 - val_accuracy: 0.7416 - val_recall: 0.6370 - val_precision: 0.8480 - val_AUROC: 0.9599 - val_AUPRC: 0.8192 - val_f1_score: 0.7275 - val_balanced_accuracy: 0.8121 - val_specificity: 0.9873 - val_miss_rate: 0.3630 - val_fall_out: 0.0127 - val_mcc: 0.7105
Epoch 15/100
63/63 [==============================] - 4s 66ms/step - loss: 0.7086 - accuracy: 0.7702 - recall: 0.6764 - precision: 0.8586 - AUROC: 0.9700 - AUPRC: 0.8552 - f1_score: 0.7567 - balanced_accuracy: 0.8320 - specificity: 0.9876 - miss_rate: 0.3236 - fall_out: 0.0124 - mcc: 0.7395 - val_loss: 0.8753 - val_accuracy: 0.7281 - val_recall: 0.6640 - val_precision: 0.8185 - val_AUROC: 0.9559 - val_AUPRC: 0.8171 - val_f1_score: 0.7332 - val_balanced_accuracy: 0.8238 - val_specificity: 0.9836 - val_miss_rate: 0.3360 - val_fall_out: 0.0164 - val_mcc: 0.7116
250/250 [==============================] - 2s 9ms/step - loss: 0.4462 - accuracy: 0.8545 - recall: 0.7812 - precision: 0.9301 - AUROC: 0.9888 - AUPRC: 0.9363 - f1_score: 0.8491 - balanced_accuracy: 0.8873 - specificity: 0.9935 - miss_rate: 0.2188 - fall_out: 0.0065 - mcc: 0.8378
63/63 [==============================] - 1s 9ms/step - loss: 0.8753 - accuracy: 0.7281 - recall: 0.6640 - precision: 0.8185 - AUROC: 0.9559 - AUPRC: 0.8171 - f1_score: 0.7332 - balanced_accuracy: 0.8238 - specificity: 0.9836 - miss_rate: 0.3360 - fall_out: 0.0164 - mcc: 0.7116
10it [11:50, 71.10s/it]
CNN_metrics_estimate = model_metrics_holdout_estimate(CNN_MelS_3s_metrics, number_of_splits)
print(f"CNN Metrics - {number_of_splits}-holdouts estimate:")
print(f"Accuracy : train - {CNN_metrics_estimate['accuracy_train']} -- test - {CNN_metrics_estimate['accuracy_test']}")
print(f"AUROC : train - {CNN_metrics_estimate['AUROC_train']} -- test - {CNN_metrics_estimate['AUROC_test']}")
print(f"AUPRC : train - {CNN_metrics_estimate['AUPRC_train']} -- test - {CNN_metrics_estimate['AUPRC_test']}")
print("-"*80)
print("CNN - Train history:")
plot_train_history(CNN_MelS_3s_history)
print("-"*100)
CNN Metrics - 10-holdouts estimate: Accuracy : train - 0.8564879775047303 -- test - 0.7238357603549957 AUROC : train - 0.9883146703243255 -- test - 0.9539275467395782 AUPRC : train - 0.9345849871635437 -- test - 0.8043521642684937 -------------------------------------------------------------------------------- CNN - Train history:
----------------------------------------------------------------------------------------------------
data['mfccs'][22].shape
(20, 130)
from speechpy.processing import cmvnw
print("---- 3s window Mfccs - Fixed CNN ----")
normalized_input_data = [cmvnw(np.array(x), win_size=301, variance_normalization=True) for x in data['mfccs']]
input_data = [np.expand_dims(x, axis=-1) for x in normalized_input_data]
data_labels = data['labels_3s']
CNN_mfccs_3s_metrics = []
CNN_mfccs_3s_history = []
# Generate holdouts
for holdout_number, (train_indices, test_indices) in enumerate(tqdm(holdouts_generator.split(input_data, data_labels))):
print(f"-- HOLDOUT {holdout_number+1}")
# Train/Test data
x_train, x_test = np.array([input_data[x] for x in train_indices]), np.array([input_data[x] for x in test_indices])
y_train, y_test = data_labels.iloc[train_indices], data_labels.iloc[test_indices]
# One-hot encoding
y_train = one_hot_encoding(y_train, 10)
y_test = one_hot_encoding(y_test, 10)
# Build CNN with best set of hyperparameters
CNN = build_fixed_CNN_mfccs(x_train.shape[1:])
print("- Training model:\n")
CNN_holdout_metrics, CNN_holdout_history = train_model(
CNN,
x_train,
x_test,
y_train.values,
y_test.values,
epochs,
batch_size
)
CNN_mfccs_3s_metrics.append(CNN_holdout_metrics)
CNN_mfccs_3s_history.append(CNN_holdout_history)
---- 3s window Mfccs - Fixed CNN ----
0it [00:00, ?it/s]
-- HOLDOUT 1
Model: "CNN_Mfccs_3s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_304 (Conv2D) (None, 20, 130, 256) 2560
max_pooling2d_304 (MaxPooli (None, 10, 65, 256) 0
ng2D)
conv2d_305 (Conv2D) (None, 10, 65, 256) 590080
max_pooling2d_305 (MaxPooli (None, 5, 33, 256) 0
ng2D)
conv2d_306 (Conv2D) (None, 5, 33, 256) 590080
max_pooling2d_306 (MaxPooli (None, 3, 17, 256) 0
ng2D)
conv2d_307 (Conv2D) (None, 3, 17, 512) 1180160
max_pooling2d_307 (MaxPooli (None, 2, 9, 512) 0
ng2D)
flatten_76 (Flatten) (None, 9216) 0
dense_228 (Dense) (None, 256) 2359552
dropout_350 (Dropout) (None, 256) 0
dense_229 (Dense) (None, 256) 65792
dropout_351 (Dropout) (None, 256) 0
dense_230 (Dense) (None, 10) 2570
=================================================================
Total params: 4,790,794
Trainable params: 4,790,794
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 5s 59ms/step - loss: 2.0584 - accuracy: 0.2199 - recall: 0.0584 - precision: 0.6332 - AUROC: 0.7015 - AUPRC: 0.2429 - f1_score: 0.1069 - balanced_accuracy: 0.5273 - specificity: 0.9962 - miss_rate: 0.9416 - fall_out: 0.0038 - mcc: 0.1714 - val_loss: 1.8018 - val_accuracy: 0.3535 - val_recall: 0.0696 - val_precision: 0.9720 - val_AUROC: 0.8089 - val_AUPRC: 0.4057 - val_f1_score: 0.1299 - val_balanced_accuracy: 0.5347 - val_specificity: 0.9998 - val_miss_rate: 0.9304 - val_fall_out: 2.2256e-04 - val_mcc: 0.2469
Epoch 2/100
63/63 [==============================] - 3s 51ms/step - loss: 1.7001 - accuracy: 0.3605 - recall: 0.1604 - precision: 0.6887 - AUROC: 0.8227 - AUPRC: 0.3987 - f1_score: 0.2603 - balanced_accuracy: 0.5762 - specificity: 0.9919 - miss_rate: 0.8396 - fall_out: 0.0081 - mcc: 0.3031 - val_loss: 1.5314 - val_accuracy: 0.4231 - val_recall: 0.1622 - val_precision: 0.7535 - val_AUROC: 0.8666 - val_AUPRC: 0.4800 - val_f1_score: 0.2670 - val_balanced_accuracy: 0.5782 - val_specificity: 0.9941 - val_miss_rate: 0.8378 - val_fall_out: 0.0059 - val_mcc: 0.3231
Epoch 3/100
63/63 [==============================] - 3s 52ms/step - loss: 1.5040 - accuracy: 0.4294 - recall: 0.2159 - precision: 0.7162 - AUROC: 0.8670 - AUPRC: 0.4829 - f1_score: 0.3318 - balanced_accuracy: 0.6032 - specificity: 0.9905 - miss_rate: 0.7841 - fall_out: 0.0095 - mcc: 0.3622 - val_loss: 1.2845 - val_accuracy: 0.5078 - val_recall: 0.2609 - val_precision: 0.8115 - val_AUROC: 0.9092 - val_AUPRC: 0.5981 - val_f1_score: 0.3948 - val_balanced_accuracy: 0.6271 - val_specificity: 0.9933 - val_miss_rate: 0.7391 - val_fall_out: 0.0067 - val_mcc: 0.4323
Epoch 4/100
63/63 [==============================] - 3s 52ms/step - loss: 1.3846 - accuracy: 0.4734 - recall: 0.2670 - precision: 0.7220 - AUROC: 0.8901 - AUPRC: 0.5387 - f1_score: 0.3899 - balanced_accuracy: 0.6278 - specificity: 0.9886 - miss_rate: 0.7330 - fall_out: 0.0114 - mcc: 0.4063 - val_loss: 1.3508 - val_accuracy: 0.5018 - val_recall: 0.3140 - val_precision: 0.6905 - val_AUROC: 0.8959 - val_AUPRC: 0.5625 - val_f1_score: 0.4317 - val_balanced_accuracy: 0.6492 - val_specificity: 0.9844 - val_miss_rate: 0.6860 - val_fall_out: 0.0156 - val_mcc: 0.4296
Epoch 5/100
63/63 [==============================] - 3s 51ms/step - loss: 1.2891 - accuracy: 0.5266 - recall: 0.3200 - precision: 0.7447 - AUROC: 0.9051 - AUPRC: 0.5913 - f1_score: 0.4477 - balanced_accuracy: 0.6539 - specificity: 0.9878 - miss_rate: 0.6800 - fall_out: 0.0122 - mcc: 0.4554 - val_loss: 1.1529 - val_accuracy: 0.5658 - val_recall: 0.3550 - val_precision: 0.7869 - val_AUROC: 0.9267 - val_AUPRC: 0.6528 - val_f1_score: 0.4893 - val_balanced_accuracy: 0.6722 - val_specificity: 0.9893 - val_miss_rate: 0.6450 - val_fall_out: 0.0107 - val_mcc: 0.4977
Epoch 6/100
63/63 [==============================] - 3s 52ms/step - loss: 1.1529 - accuracy: 0.5787 - recall: 0.3885 - precision: 0.7426 - AUROC: 0.9253 - AUPRC: 0.6483 - f1_score: 0.5102 - balanced_accuracy: 0.6868 - specificity: 0.9850 - miss_rate: 0.6115 - fall_out: 0.0150 - mcc: 0.5033 - val_loss: 1.1289 - val_accuracy: 0.5874 - val_recall: 0.4006 - val_precision: 0.7540 - val_AUROC: 0.9281 - val_AUPRC: 0.6581 - val_f1_score: 0.5232 - val_balanced_accuracy: 0.6930 - val_specificity: 0.9855 - val_miss_rate: 0.5994 - val_fall_out: 0.0145 - val_mcc: 0.5164
Epoch 7/100
63/63 [==============================] - 3s 51ms/step - loss: 1.1015 - accuracy: 0.6090 - recall: 0.4380 - precision: 0.7607 - AUROC: 0.9316 - AUPRC: 0.6814 - f1_score: 0.5559 - balanced_accuracy: 0.7113 - specificity: 0.9847 - miss_rate: 0.5620 - fall_out: 0.0153 - mcc: 0.5444 - val_loss: 1.0082 - val_accuracy: 0.6350 - val_recall: 0.4837 - val_precision: 0.7553 - val_AUROC: 0.9427 - val_AUPRC: 0.7141 - val_f1_score: 0.5897 - val_balanced_accuracy: 0.7332 - val_specificity: 0.9826 - val_miss_rate: 0.5163 - val_fall_out: 0.0174 - val_mcc: 0.5714
Epoch 8/100
63/63 [==============================] - 3s 51ms/step - loss: 1.0389 - accuracy: 0.6387 - recall: 0.4787 - precision: 0.7576 - AUROC: 0.9391 - AUPRC: 0.7069 - f1_score: 0.5867 - balanced_accuracy: 0.7308 - specificity: 0.9830 - miss_rate: 0.5213 - fall_out: 0.0170 - mcc: 0.5693 - val_loss: 0.9750 - val_accuracy: 0.6455 - val_recall: 0.5388 - val_precision: 0.7462 - val_AUROC: 0.9464 - val_AUPRC: 0.7311 - val_f1_score: 0.6258 - val_balanced_accuracy: 0.7592 - val_specificity: 0.9796 - val_miss_rate: 0.4612 - val_fall_out: 0.0204 - val_mcc: 0.6009
Epoch 9/100
63/63 [==============================] - 3s 50ms/step - loss: 0.9290 - accuracy: 0.6822 - recall: 0.5542 - precision: 0.7895 - AUROC: 0.9506 - AUPRC: 0.7563 - f1_score: 0.6513 - balanced_accuracy: 0.7689 - specificity: 0.9836 - miss_rate: 0.4458 - fall_out: 0.0164 - mcc: 0.6315 - val_loss: 0.8220 - val_accuracy: 0.7116 - val_recall: 0.6099 - val_precision: 0.8115 - val_AUROC: 0.9615 - val_AUPRC: 0.8044 - val_f1_score: 0.6964 - val_balanced_accuracy: 0.7971 - val_specificity: 0.9843 - val_miss_rate: 0.3901 - val_fall_out: 0.0157 - val_mcc: 0.6761
Epoch 10/100
63/63 [==============================] - 3s 50ms/step - loss: 0.8494 - accuracy: 0.7095 - recall: 0.5967 - precision: 0.8128 - AUROC: 0.9583 - AUPRC: 0.7889 - f1_score: 0.6882 - balanced_accuracy: 0.7907 - specificity: 0.9847 - miss_rate: 0.4033 - fall_out: 0.0153 - mcc: 0.6688 - val_loss: 0.9208 - val_accuracy: 0.6865 - val_recall: 0.6124 - val_precision: 0.7625 - val_AUROC: 0.9509 - val_AUPRC: 0.7717 - val_f1_score: 0.6793 - val_balanced_accuracy: 0.7956 - val_specificity: 0.9788 - val_miss_rate: 0.3876 - val_fall_out: 0.0212 - val_mcc: 0.6526
Epoch 11/100
63/63 [==============================] - 3s 51ms/step - loss: 0.7671 - accuracy: 0.7366 - recall: 0.6415 - precision: 0.8333 - AUROC: 0.9654 - AUPRC: 0.8267 - f1_score: 0.7249 - balanced_accuracy: 0.8136 - specificity: 0.9857 - miss_rate: 0.3585 - fall_out: 0.0143 - mcc: 0.7059 - val_loss: 0.8358 - val_accuracy: 0.7141 - val_recall: 0.6249 - val_precision: 0.8088 - val_AUROC: 0.9593 - val_AUPRC: 0.8009 - val_f1_score: 0.7051 - val_balanced_accuracy: 0.8043 - val_specificity: 0.9836 - val_miss_rate: 0.3751 - val_fall_out: 0.0164 - val_mcc: 0.6837
250/250 [==============================] - 2s 8ms/step - loss: 0.6422 - accuracy: 0.7739 - recall: 0.6831 - precision: 0.8533 - AUROC: 0.9765 - AUPRC: 0.8637 - f1_score: 0.7588 - balanced_accuracy: 0.8350 - specificity: 0.9869 - miss_rate: 0.3169 - fall_out: 0.0131 - mcc: 0.7407
63/63 [==============================] - 1s 8ms/step - loss: 0.8358 - accuracy: 0.7141 - recall: 0.6249 - precision: 0.8088 - AUROC: 0.9593 - AUPRC: 0.8009 - f1_score: 0.7051 - balanced_accuracy: 0.8043 - specificity: 0.9836 - miss_rate: 0.3751 - fall_out: 0.0164 - mcc: 0.6837
1it [00:39, 39.69s/it]
-- HOLDOUT 2
Model: "CNN_Mfccs_3s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_308 (Conv2D) (None, 20, 130, 256) 2560
max_pooling2d_308 (MaxPooli (None, 10, 65, 256) 0
ng2D)
conv2d_309 (Conv2D) (None, 10, 65, 256) 590080
max_pooling2d_309 (MaxPooli (None, 5, 33, 256) 0
ng2D)
conv2d_310 (Conv2D) (None, 5, 33, 256) 590080
max_pooling2d_310 (MaxPooli (None, 3, 17, 256) 0
ng2D)
conv2d_311 (Conv2D) (None, 3, 17, 512) 1180160
max_pooling2d_311 (MaxPooli (None, 2, 9, 512) 0
ng2D)
flatten_77 (Flatten) (None, 9216) 0
dense_231 (Dense) (None, 256) 2359552
dropout_352 (Dropout) (None, 256) 0
dense_232 (Dense) (None, 256) 65792
dropout_353 (Dropout) (None, 256) 0
dense_233 (Dense) (None, 10) 2570
=================================================================
Total params: 4,790,794
Trainable params: 4,790,794
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 5s 59ms/step - loss: 2.0058 - accuracy: 0.2495 - recall: 0.0728 - precision: 0.5977 - AUROC: 0.7218 - AUPRC: 0.2673 - f1_score: 0.1297 - balanced_accuracy: 0.5337 - specificity: 0.9946 - miss_rate: 0.9272 - fall_out: 0.0054 - mcc: 0.1842 - val_loss: 1.6741 - val_accuracy: 0.3746 - val_recall: 0.1803 - val_precision: 0.7660 - val_AUROC: 0.8277 - val_AUPRC: 0.4227 - val_f1_score: 0.2919 - val_balanced_accuracy: 0.5871 - val_specificity: 0.9939 - val_miss_rate: 0.8197 - val_fall_out: 0.0061 - val_mcc: 0.3446
Epoch 2/100
63/63 [==============================] - 3s 51ms/step - loss: 1.7305 - accuracy: 0.3478 - recall: 0.1429 - precision: 0.6645 - AUROC: 0.8170 - AUPRC: 0.3808 - f1_score: 0.2352 - balanced_accuracy: 0.5674 - specificity: 0.9920 - miss_rate: 0.8571 - fall_out: 0.0080 - mcc: 0.2790 - val_loss: 1.5132 - val_accuracy: 0.4301 - val_recall: 0.2028 - val_precision: 0.7729 - val_AUROC: 0.8669 - val_AUPRC: 0.4859 - val_f1_score: 0.3213 - val_balanced_accuracy: 0.5981 - val_specificity: 0.9934 - val_miss_rate: 0.7972 - val_fall_out: 0.0066 - val_mcc: 0.3682
Epoch 3/100
63/63 [==============================] - 3s 51ms/step - loss: 1.5209 - accuracy: 0.4282 - recall: 0.2030 - precision: 0.6990 - AUROC: 0.8639 - AUPRC: 0.4713 - f1_score: 0.3147 - balanced_accuracy: 0.5967 - specificity: 0.9903 - miss_rate: 0.7970 - fall_out: 0.0097 - mcc: 0.3453 - val_loss: 1.3360 - val_accuracy: 0.5098 - val_recall: 0.2173 - val_precision: 0.8697 - val_AUROC: 0.8980 - val_AUPRC: 0.5864 - val_f1_score: 0.3478 - val_balanced_accuracy: 0.6069 - val_specificity: 0.9964 - val_miss_rate: 0.7827 - val_fall_out: 0.0036 - val_mcc: 0.4108
Epoch 4/100
63/63 [==============================] - 3s 51ms/step - loss: 1.3878 - accuracy: 0.4828 - recall: 0.2594 - precision: 0.7171 - AUROC: 0.8893 - AUPRC: 0.5367 - f1_score: 0.3810 - balanced_accuracy: 0.6240 - specificity: 0.9886 - miss_rate: 0.7406 - fall_out: 0.0114 - mcc: 0.3985 - val_loss: 1.2945 - val_accuracy: 0.5173 - val_recall: 0.3185 - val_precision: 0.7353 - val_AUROC: 0.9043 - val_AUPRC: 0.5868 - val_f1_score: 0.4444 - val_balanced_accuracy: 0.6529 - val_specificity: 0.9873 - val_miss_rate: 0.6815 - val_fall_out: 0.0127 - val_mcc: 0.4506
Epoch 5/100
63/63 [==============================] - 3s 50ms/step - loss: 1.2802 - accuracy: 0.5177 - recall: 0.3148 - precision: 0.7344 - AUROC: 0.9072 - AUPRC: 0.5853 - f1_score: 0.4406 - balanced_accuracy: 0.6511 - specificity: 0.9873 - miss_rate: 0.6852 - fall_out: 0.0127 - mcc: 0.4475 - val_loss: 1.1875 - val_accuracy: 0.5638 - val_recall: 0.3570 - val_precision: 0.7742 - val_AUROC: 0.9198 - val_AUPRC: 0.6427 - val_f1_score: 0.4887 - val_balanced_accuracy: 0.6727 - val_specificity: 0.9884 - val_miss_rate: 0.6430 - val_fall_out: 0.0116 - val_mcc: 0.4941
Epoch 6/100
63/63 [==============================] - 3s 51ms/step - loss: 1.1772 - accuracy: 0.5683 - recall: 0.3798 - precision: 0.7479 - AUROC: 0.9216 - AUPRC: 0.6387 - f1_score: 0.5037 - balanced_accuracy: 0.6828 - specificity: 0.9858 - miss_rate: 0.6202 - fall_out: 0.0142 - mcc: 0.4995 - val_loss: 1.1616 - val_accuracy: 0.5789 - val_recall: 0.3410 - val_precision: 0.8534 - val_AUROC: 0.9249 - val_AUPRC: 0.6680 - val_f1_score: 0.4873 - val_balanced_accuracy: 0.6673 - val_specificity: 0.9935 - val_miss_rate: 0.6590 - val_fall_out: 0.0065 - val_mcc: 0.5123
Epoch 7/100
63/63 [==============================] - 3s 51ms/step - loss: 1.1299 - accuracy: 0.5938 - recall: 0.4108 - precision: 0.7542 - AUROC: 0.9279 - AUPRC: 0.6596 - f1_score: 0.5319 - balanced_accuracy: 0.6980 - specificity: 0.9851 - miss_rate: 0.5892 - fall_out: 0.0149 - mcc: 0.5234 - val_loss: 0.9741 - val_accuracy: 0.6480 - val_recall: 0.4787 - val_precision: 0.7901 - val_AUROC: 0.9473 - val_AUPRC: 0.7334 - val_f1_score: 0.5962 - val_balanced_accuracy: 0.7323 - val_specificity: 0.9859 - val_miss_rate: 0.5213 - val_fall_out: 0.0141 - val_mcc: 0.5842
Epoch 8/100
63/63 [==============================] - 3s 53ms/step - loss: 1.0527 - accuracy: 0.6199 - recall: 0.4647 - precision: 0.7584 - AUROC: 0.9375 - AUPRC: 0.6965 - f1_score: 0.5763 - balanced_accuracy: 0.7241 - specificity: 0.9836 - miss_rate: 0.5353 - fall_out: 0.0164 - mcc: 0.5607 - val_loss: 0.9783 - val_accuracy: 0.6299 - val_recall: 0.4877 - val_precision: 0.7786 - val_AUROC: 0.9469 - val_AUPRC: 0.7326 - val_f1_score: 0.5998 - val_balanced_accuracy: 0.7362 - val_specificity: 0.9846 - val_miss_rate: 0.5123 - val_fall_out: 0.0154 - val_mcc: 0.5847
Epoch 9/100
63/63 [==============================] - 3s 51ms/step - loss: 0.9292 - accuracy: 0.6746 - recall: 0.5382 - precision: 0.7947 - AUROC: 0.9512 - AUPRC: 0.7567 - f1_score: 0.6418 - balanced_accuracy: 0.7614 - specificity: 0.9846 - miss_rate: 0.4618 - fall_out: 0.0154 - mcc: 0.6241 - val_loss: 0.8414 - val_accuracy: 0.6990 - val_recall: 0.5804 - val_precision: 0.8071 - val_AUROC: 0.9601 - val_AUPRC: 0.7943 - val_f1_score: 0.6752 - val_balanced_accuracy: 0.7825 - val_specificity: 0.9846 - val_miss_rate: 0.4196 - val_fall_out: 0.0154 - val_mcc: 0.6561
Epoch 10/100
63/63 [==============================] - 3s 52ms/step - loss: 0.8595 - accuracy: 0.7118 - recall: 0.5912 - precision: 0.8106 - AUROC: 0.9572 - AUPRC: 0.7852 - f1_score: 0.6837 - balanced_accuracy: 0.7879 - specificity: 0.9846 - miss_rate: 0.4088 - fall_out: 0.0154 - mcc: 0.6644 - val_loss: 1.0368 - val_accuracy: 0.6510 - val_recall: 0.5754 - val_precision: 0.7347 - val_AUROC: 0.9385 - val_AUPRC: 0.7120 - val_f1_score: 0.6453 - val_balanced_accuracy: 0.7761 - val_specificity: 0.9769 - val_miss_rate: 0.4246 - val_fall_out: 0.0231 - val_mcc: 0.6167
Epoch 11/100
63/63 [==============================] - 3s 49ms/step - loss: 0.7929 - accuracy: 0.7278 - recall: 0.6253 - precision: 0.8220 - AUROC: 0.9634 - AUPRC: 0.8138 - f1_score: 0.7103 - balanced_accuracy: 0.8051 - specificity: 0.9850 - miss_rate: 0.3747 - fall_out: 0.0150 - mcc: 0.6905 - val_loss: 0.8294 - val_accuracy: 0.7191 - val_recall: 0.6309 - val_precision: 0.8051 - val_AUROC: 0.9603 - val_AUPRC: 0.8000 - val_f1_score: 0.7075 - val_balanced_accuracy: 0.8070 - val_specificity: 0.9830 - val_miss_rate: 0.3691 - val_fall_out: 0.0170 - val_mcc: 0.6854
Epoch 12/100
63/63 [==============================] - 3s 51ms/step - loss: 0.7454 - accuracy: 0.7457 - recall: 0.6543 - precision: 0.8357 - AUROC: 0.9677 - AUPRC: 0.8328 - f1_score: 0.7340 - balanced_accuracy: 0.8200 - specificity: 0.9857 - miss_rate: 0.3457 - fall_out: 0.0143 - mcc: 0.7147 - val_loss: 0.7498 - val_accuracy: 0.7411 - val_recall: 0.6600 - val_precision: 0.8274 - val_AUROC: 0.9666 - val_AUPRC: 0.8349 - val_f1_score: 0.7343 - val_balanced_accuracy: 0.8223 - val_specificity: 0.9847 - val_miss_rate: 0.3400 - val_fall_out: 0.0153 - val_mcc: 0.7138
Epoch 13/100
63/63 [==============================] - 3s 50ms/step - loss: 0.6774 - accuracy: 0.7704 - recall: 0.6904 - precision: 0.8487 - AUROC: 0.9723 - AUPRC: 0.8555 - f1_score: 0.7614 - balanced_accuracy: 0.8384 - specificity: 0.9863 - miss_rate: 0.3096 - fall_out: 0.0137 - mcc: 0.7426 - val_loss: 0.7582 - val_accuracy: 0.7486 - val_recall: 0.6920 - val_precision: 0.8236 - val_AUROC: 0.9648 - val_AUPRC: 0.8369 - val_f1_score: 0.7521 - val_balanced_accuracy: 0.8378 - val_specificity: 0.9835 - val_miss_rate: 0.3080 - val_fall_out: 0.0165 - val_mcc: 0.7305
Epoch 14/100
63/63 [==============================] - 3s 52ms/step - loss: 0.5786 - accuracy: 0.7993 - recall: 0.7367 - precision: 0.8726 - AUROC: 0.9799 - AUPRC: 0.8900 - f1_score: 0.7989 - balanced_accuracy: 0.8624 - specificity: 0.9880 - miss_rate: 0.2633 - fall_out: 0.0120 - mcc: 0.7820 - val_loss: 0.8744 - val_accuracy: 0.7321 - val_recall: 0.6795 - val_precision: 0.7936 - val_AUROC: 0.9561 - val_AUPRC: 0.8075 - val_f1_score: 0.7321 - val_balanced_accuracy: 0.8299 - val_specificity: 0.9804 - val_miss_rate: 0.3205 - val_fall_out: 0.0196 - val_mcc: 0.7075
250/250 [==============================] - 2s 8ms/step - loss: 0.5824 - accuracy: 0.8022 - recall: 0.7533 - precision: 0.8607 - AUROC: 0.9794 - AUPRC: 0.8937 - f1_score: 0.8034 - balanced_accuracy: 0.8699 - specificity: 0.9865 - miss_rate: 0.2467 - fall_out: 0.0135 - mcc: 0.7853
63/63 [==============================] - 1s 9ms/step - loss: 0.8744 - accuracy: 0.7321 - recall: 0.6795 - precision: 0.7936 - AUROC: 0.9561 - AUPRC: 0.8075 - f1_score: 0.7321 - balanced_accuracy: 0.8299 - specificity: 0.9804 - miss_rate: 0.3205 - fall_out: 0.0196 - mcc: 0.7075
2it [01:28, 45.29s/it]
-- HOLDOUT 3
Model: "CNN_Mfccs_3s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_312 (Conv2D) (None, 20, 130, 256) 2560
max_pooling2d_312 (MaxPooli (None, 10, 65, 256) 0
ng2D)
conv2d_313 (Conv2D) (None, 10, 65, 256) 590080
max_pooling2d_313 (MaxPooli (None, 5, 33, 256) 0
ng2D)
conv2d_314 (Conv2D) (None, 5, 33, 256) 590080
max_pooling2d_314 (MaxPooli (None, 3, 17, 256) 0
ng2D)
conv2d_315 (Conv2D) (None, 3, 17, 512) 1180160
max_pooling2d_315 (MaxPooli (None, 2, 9, 512) 0
ng2D)
flatten_78 (Flatten) (None, 9216) 0
dense_234 (Dense) (None, 256) 2359552
dropout_354 (Dropout) (None, 256) 0
dense_235 (Dense) (None, 256) 65792
dropout_355 (Dropout) (None, 256) 0
dense_236 (Dense) (None, 10) 2570
=================================================================
Total params: 4,790,794
Trainable params: 4,790,794
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 5s 60ms/step - loss: 2.0453 - accuracy: 0.2370 - recall: 0.0547 - precision: 0.5574 - AUROC: 0.7064 - AUPRC: 0.2418 - f1_score: 0.0997 - balanced_accuracy: 0.5250 - specificity: 0.9952 - miss_rate: 0.9453 - fall_out: 0.0048 - mcc: 0.1518 - val_loss: 1.6939 - val_accuracy: 0.3420 - val_recall: 0.1277 - val_precision: 0.7681 - val_AUROC: 0.8311 - val_AUPRC: 0.4156 - val_f1_score: 0.2190 - val_balanced_accuracy: 0.5617 - val_specificity: 0.9957 - val_miss_rate: 0.8723 - val_fall_out: 0.0043 - val_mcc: 0.2895
Epoch 2/100
63/63 [==============================] - 3s 52ms/step - loss: 1.6536 - accuracy: 0.3781 - recall: 0.1690 - precision: 0.6900 - AUROC: 0.8342 - AUPRC: 0.4119 - f1_score: 0.2715 - balanced_accuracy: 0.5803 - specificity: 0.9916 - miss_rate: 0.8310 - fall_out: 0.0084 - mcc: 0.3116 - val_loss: 1.4700 - val_accuracy: 0.4326 - val_recall: 0.2153 - val_precision: 0.7651 - val_AUROC: 0.8762 - val_AUPRC: 0.5031 - val_f1_score: 0.3361 - val_balanced_accuracy: 0.6040 - val_specificity: 0.9927 - val_miss_rate: 0.7847 - val_fall_out: 0.0073 - val_mcc: 0.3773
Epoch 3/100
63/63 [==============================] - 4s 56ms/step - loss: 1.4879 - accuracy: 0.4372 - recall: 0.2159 - precision: 0.7213 - AUROC: 0.8703 - AUPRC: 0.4864 - f1_score: 0.3324 - balanced_accuracy: 0.6033 - specificity: 0.9907 - miss_rate: 0.7841 - fall_out: 0.0093 - mcc: 0.3638 - val_loss: 1.3667 - val_accuracy: 0.4817 - val_recall: 0.2328 - val_precision: 0.7635 - val_AUROC: 0.8927 - val_AUPRC: 0.5387 - val_f1_score: 0.3569 - val_balanced_accuracy: 0.6124 - val_specificity: 0.9920 - val_miss_rate: 0.7672 - val_fall_out: 0.0080 - val_mcc: 0.3923
Epoch 4/100
63/63 [==============================] - 3s 50ms/step - loss: 1.3391 - accuracy: 0.5008 - recall: 0.2854 - precision: 0.7198 - AUROC: 0.8975 - AUPRC: 0.5596 - f1_score: 0.4088 - balanced_accuracy: 0.6366 - specificity: 0.9877 - miss_rate: 0.7146 - fall_out: 0.0123 - mcc: 0.4198 - val_loss: 1.2850 - val_accuracy: 0.5098 - val_recall: 0.3145 - val_precision: 0.7441 - val_AUROC: 0.9074 - val_AUPRC: 0.5922 - val_f1_score: 0.4421 - val_balanced_accuracy: 0.6512 - val_specificity: 0.9880 - val_miss_rate: 0.6855 - val_fall_out: 0.0120 - val_mcc: 0.4510
Epoch 5/100
63/63 [==============================] - 3s 49ms/step - loss: 1.2157 - accuracy: 0.5542 - recall: 0.3608 - precision: 0.7431 - AUROC: 0.9163 - AUPRC: 0.6221 - f1_score: 0.4858 - balanced_accuracy: 0.6735 - specificity: 0.9861 - miss_rate: 0.6392 - fall_out: 0.0139 - mcc: 0.4843 - val_loss: 1.1567 - val_accuracy: 0.5909 - val_recall: 0.3630 - val_precision: 0.7941 - val_AUROC: 0.9262 - val_AUPRC: 0.6599 - val_f1_score: 0.4983 - val_balanced_accuracy: 0.6763 - val_specificity: 0.9895 - val_miss_rate: 0.6370 - val_fall_out: 0.0105 - val_mcc: 0.5064
Epoch 6/100
63/63 [==============================] - 3s 49ms/step - loss: 1.1171 - accuracy: 0.5978 - recall: 0.4230 - precision: 0.7606 - AUROC: 0.9297 - AUPRC: 0.6714 - f1_score: 0.5436 - balanced_accuracy: 0.7041 - specificity: 0.9852 - miss_rate: 0.5770 - fall_out: 0.0148 - mcc: 0.5343 - val_loss: 1.1226 - val_accuracy: 0.5899 - val_recall: 0.4306 - val_precision: 0.7692 - val_AUROC: 0.9290 - val_AUPRC: 0.6719 - val_f1_score: 0.5522 - val_balanced_accuracy: 0.7081 - val_specificity: 0.9856 - val_miss_rate: 0.5694 - val_fall_out: 0.0144 - val_mcc: 0.5432
Epoch 7/100
63/63 [==============================] - 3s 49ms/step - loss: 0.9875 - accuracy: 0.6536 - recall: 0.5029 - precision: 0.7836 - AUROC: 0.9447 - AUPRC: 0.7332 - f1_score: 0.6126 - balanced_accuracy: 0.7437 - specificity: 0.9846 - miss_rate: 0.4971 - fall_out: 0.0154 - mcc: 0.5967 - val_loss: 0.8954 - val_accuracy: 0.6755 - val_recall: 0.5684 - val_precision: 0.8010 - val_AUROC: 0.9546 - val_AUPRC: 0.7706 - val_f1_score: 0.6649 - val_balanced_accuracy: 0.7763 - val_specificity: 0.9843 - val_miss_rate: 0.4316 - val_fall_out: 0.0157 - val_mcc: 0.6458
Epoch 8/100
63/63 [==============================] - 3s 51ms/step - loss: 0.9296 - accuracy: 0.6849 - recall: 0.5554 - precision: 0.8037 - AUROC: 0.9506 - AUPRC: 0.7586 - f1_score: 0.6568 - balanced_accuracy: 0.7701 - specificity: 0.9849 - miss_rate: 0.4446 - fall_out: 0.0151 - mcc: 0.6391 - val_loss: 0.9753 - val_accuracy: 0.6745 - val_recall: 0.5759 - val_precision: 0.7571 - val_AUROC: 0.9448 - val_AUPRC: 0.7427 - val_f1_score: 0.6542 - val_balanced_accuracy: 0.7777 - val_specificity: 0.9795 - val_miss_rate: 0.4241 - val_fall_out: 0.0205 - val_mcc: 0.6284
Epoch 9/100
63/63 [==============================] - 4s 57ms/step - loss: 0.8540 - accuracy: 0.7112 - recall: 0.5936 - precision: 0.8186 - AUROC: 0.9578 - AUPRC: 0.7903 - f1_score: 0.6882 - balanced_accuracy: 0.7895 - specificity: 0.9854 - miss_rate: 0.4064 - fall_out: 0.0146 - mcc: 0.6698 - val_loss: 0.8828 - val_accuracy: 0.6935 - val_recall: 0.6069 - val_precision: 0.7875 - val_AUROC: 0.9555 - val_AUPRC: 0.7786 - val_f1_score: 0.6855 - val_balanced_accuracy: 0.7944 - val_specificity: 0.9818 - val_miss_rate: 0.3931 - val_fall_out: 0.0182 - val_mcc: 0.6622
Epoch 10/100
63/63 [==============================] - 3s 53ms/step - loss: 0.7985 - accuracy: 0.7336 - recall: 0.6290 - precision: 0.8323 - AUROC: 0.9631 - AUPRC: 0.8158 - f1_score: 0.7165 - balanced_accuracy: 0.8075 - specificity: 0.9859 - miss_rate: 0.3710 - fall_out: 0.0141 - mcc: 0.6979 - val_loss: 0.7301 - val_accuracy: 0.7446 - val_recall: 0.6700 - val_precision: 0.8234 - val_AUROC: 0.9685 - val_AUPRC: 0.8423 - val_f1_score: 0.7388 - val_balanced_accuracy: 0.8270 - val_specificity: 0.9840 - val_miss_rate: 0.3300 - val_fall_out: 0.0160 - val_mcc: 0.7177
Epoch 11/100
63/63 [==============================] - 3s 50ms/step - loss: 0.6934 - accuracy: 0.7653 - recall: 0.6789 - precision: 0.8511 - AUROC: 0.9717 - AUPRC: 0.8521 - f1_score: 0.7553 - balanced_accuracy: 0.8328 - specificity: 0.9868 - miss_rate: 0.3211 - fall_out: 0.0132 - mcc: 0.7371 - val_loss: 0.7281 - val_accuracy: 0.7566 - val_recall: 0.7031 - val_precision: 0.8235 - val_AUROC: 0.9673 - val_AUPRC: 0.8452 - val_f1_score: 0.7585 - val_balanced_accuracy: 0.8432 - val_specificity: 0.9833 - val_miss_rate: 0.2969 - val_fall_out: 0.0167 - val_mcc: 0.7368
Epoch 12/100
63/63 [==============================] - 3s 50ms/step - loss: 0.6075 - accuracy: 0.7993 - recall: 0.7288 - precision: 0.8694 - AUROC: 0.9771 - AUPRC: 0.8810 - f1_score: 0.7929 - balanced_accuracy: 0.8583 - specificity: 0.9878 - miss_rate: 0.2712 - fall_out: 0.0122 - mcc: 0.7758 - val_loss: 0.7564 - val_accuracy: 0.7466 - val_recall: 0.6720 - val_precision: 0.8284 - val_AUROC: 0.9661 - val_AUPRC: 0.8357 - val_f1_score: 0.7421 - val_balanced_accuracy: 0.8283 - val_specificity: 0.9845 - val_miss_rate: 0.3280 - val_fall_out: 0.0155 - val_mcc: 0.7214
Epoch 13/100
63/63 [==============================] - 3s 49ms/step - loss: 0.5408 - accuracy: 0.8267 - recall: 0.7655 - precision: 0.8859 - AUROC: 0.9815 - AUPRC: 0.9035 - f1_score: 0.8213 - balanced_accuracy: 0.8773 - specificity: 0.9890 - miss_rate: 0.2345 - fall_out: 0.0110 - mcc: 0.8057 - val_loss: 0.7778 - val_accuracy: 0.7436 - val_recall: 0.6895 - val_precision: 0.8124 - val_AUROC: 0.9645 - val_AUPRC: 0.8296 - val_f1_score: 0.7459 - val_balanced_accuracy: 0.8359 - val_specificity: 0.9823 - val_miss_rate: 0.3105 - val_fall_out: 0.0177 - val_mcc: 0.7232
250/250 [==============================] - 2s 7ms/step - loss: 0.4874 - accuracy: 0.8258 - recall: 0.7720 - precision: 0.8811 - AUROC: 0.9863 - AUPRC: 0.9156 - f1_score: 0.8230 - balanced_accuracy: 0.8802 - specificity: 0.9884 - miss_rate: 0.2280 - fall_out: 0.0116 - mcc: 0.8069
63/63 [==============================] - 0s 7ms/step - loss: 0.7778 - accuracy: 0.7436 - recall: 0.6895 - precision: 0.8124 - AUROC: 0.9645 - AUPRC: 0.8296 - f1_score: 0.7459 - balanced_accuracy: 0.8359 - specificity: 0.9823 - miss_rate: 0.3105 - fall_out: 0.0177 - mcc: 0.7232
3it [02:15, 45.83s/it]
-- HOLDOUT 4
Model: "CNN_Mfccs_3s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_316 (Conv2D) (None, 20, 130, 256) 2560
max_pooling2d_316 (MaxPooli (None, 10, 65, 256) 0
ng2D)
conv2d_317 (Conv2D) (None, 10, 65, 256) 590080
max_pooling2d_317 (MaxPooli (None, 5, 33, 256) 0
ng2D)
conv2d_318 (Conv2D) (None, 5, 33, 256) 590080
max_pooling2d_318 (MaxPooli (None, 3, 17, 256) 0
ng2D)
conv2d_319 (Conv2D) (None, 3, 17, 512) 1180160
max_pooling2d_319 (MaxPooli (None, 2, 9, 512) 0
ng2D)
flatten_79 (Flatten) (None, 9216) 0
dense_237 (Dense) (None, 256) 2359552
dropout_356 (Dropout) (None, 256) 0
dense_238 (Dense) (None, 256) 65792
dropout_357 (Dropout) (None, 256) 0
dense_239 (Dense) (None, 10) 2570
=================================================================
Total params: 4,790,794
Trainable params: 4,790,794
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 5s 59ms/step - loss: 2.0229 - accuracy: 0.2382 - recall: 0.0685 - precision: 0.6174 - AUROC: 0.7116 - AUPRC: 0.2628 - f1_score: 0.1233 - balanced_accuracy: 0.5319 - specificity: 0.9953 - miss_rate: 0.9315 - fall_out: 0.0047 - mcc: 0.1827 - val_loss: 1.6296 - val_accuracy: 0.4136 - val_recall: 0.1713 - val_precision: 0.8444 - val_AUROC: 0.8479 - val_AUPRC: 0.4606 - val_f1_score: 0.2848 - val_balanced_accuracy: 0.5839 - val_specificity: 0.9965 - val_miss_rate: 0.8287 - val_fall_out: 0.0035 - val_mcc: 0.3570
Epoch 2/100
63/63 [==============================] - 3s 50ms/step - loss: 1.6608 - accuracy: 0.3768 - recall: 0.1737 - precision: 0.6914 - AUROC: 0.8318 - AUPRC: 0.4180 - f1_score: 0.2777 - balanced_accuracy: 0.5826 - specificity: 0.9914 - miss_rate: 0.8263 - fall_out: 0.0086 - mcc: 0.3165 - val_loss: 1.4472 - val_accuracy: 0.4482 - val_recall: 0.2474 - val_precision: 0.6842 - val_AUROC: 0.8824 - val_AUPRC: 0.5144 - val_f1_score: 0.3634 - val_balanced_accuracy: 0.6173 - val_specificity: 0.9873 - val_miss_rate: 0.7526 - val_fall_out: 0.0127 - val_mcc: 0.3772
Epoch 3/100
63/63 [==============================] - 3s 50ms/step - loss: 1.4972 - accuracy: 0.4428 - recall: 0.2248 - precision: 0.7120 - AUROC: 0.8691 - AUPRC: 0.4934 - f1_score: 0.3417 - balanced_accuracy: 0.6074 - specificity: 0.9899 - miss_rate: 0.7752 - fall_out: 0.0101 - mcc: 0.3684 - val_loss: 1.3231 - val_accuracy: 0.5098 - val_recall: 0.2704 - val_precision: 0.7459 - val_AUROC: 0.9013 - val_AUPRC: 0.5715 - val_f1_score: 0.3969 - val_balanced_accuracy: 0.6301 - val_specificity: 0.9898 - val_miss_rate: 0.7296 - val_fall_out: 0.0102 - val_mcc: 0.4176
Epoch 4/100
63/63 [==============================] - 3s 50ms/step - loss: 1.3774 - accuracy: 0.4870 - recall: 0.2743 - precision: 0.7232 - AUROC: 0.8913 - AUPRC: 0.5453 - f1_score: 0.3977 - balanced_accuracy: 0.6313 - specificity: 0.9883 - miss_rate: 0.7257 - fall_out: 0.0117 - mcc: 0.4125 - val_loss: 1.1819 - val_accuracy: 0.5378 - val_recall: 0.3530 - val_precision: 0.7622 - val_AUROC: 0.9211 - val_AUPRC: 0.6307 - val_f1_score: 0.4825 - val_balanced_accuracy: 0.6704 - val_specificity: 0.9878 - val_miss_rate: 0.6470 - val_fall_out: 0.0122 - val_mcc: 0.4864
Epoch 5/100
63/63 [==============================] - 3s 50ms/step - loss: 1.2706 - accuracy: 0.5279 - recall: 0.3269 - precision: 0.7309 - AUROC: 0.9085 - AUPRC: 0.5965 - f1_score: 0.4518 - balanced_accuracy: 0.6568 - specificity: 0.9866 - miss_rate: 0.6731 - fall_out: 0.0134 - mcc: 0.4550 - val_loss: 1.1494 - val_accuracy: 0.5669 - val_recall: 0.3340 - val_precision: 0.7893 - val_AUROC: 0.9270 - val_AUPRC: 0.6508 - val_f1_score: 0.4694 - val_balanced_accuracy: 0.6620 - val_specificity: 0.9901 - val_miss_rate: 0.6660 - val_fall_out: 0.0099 - val_mcc: 0.4830
Epoch 6/100
63/63 [==============================] - 3s 50ms/step - loss: 1.1523 - accuracy: 0.5810 - recall: 0.3994 - precision: 0.7591 - AUROC: 0.9249 - AUPRC: 0.6543 - f1_score: 0.5234 - balanced_accuracy: 0.6927 - specificity: 0.9859 - miss_rate: 0.6006 - fall_out: 0.0141 - mcc: 0.5178 - val_loss: 0.9803 - val_accuracy: 0.6630 - val_recall: 0.4512 - val_precision: 0.8358 - val_AUROC: 0.9483 - val_AUPRC: 0.7392 - val_f1_score: 0.5860 - val_balanced_accuracy: 0.7207 - val_specificity: 0.9902 - val_miss_rate: 0.5488 - val_fall_out: 0.0098 - val_mcc: 0.5859
Epoch 7/100
63/63 [==============================] - 3s 50ms/step - loss: 1.0446 - accuracy: 0.6251 - recall: 0.4682 - precision: 0.7674 - AUROC: 0.9387 - AUPRC: 0.7023 - f1_score: 0.5816 - balanced_accuracy: 0.7262 - specificity: 0.9842 - miss_rate: 0.5318 - fall_out: 0.0158 - mcc: 0.5671 - val_loss: 0.9328 - val_accuracy: 0.6505 - val_recall: 0.5238 - val_precision: 0.7541 - val_AUROC: 0.9512 - val_AUPRC: 0.7378 - val_f1_score: 0.6182 - val_balanced_accuracy: 0.7524 - val_specificity: 0.9810 - val_miss_rate: 0.4762 - val_fall_out: 0.0190 - val_mcc: 0.5957
Epoch 8/100
63/63 [==============================] - 3s 50ms/step - loss: 0.9644 - accuracy: 0.6599 - recall: 0.5220 - precision: 0.7867 - AUROC: 0.9473 - AUPRC: 0.7403 - f1_score: 0.6276 - balanced_accuracy: 0.7532 - specificity: 0.9843 - miss_rate: 0.4780 - fall_out: 0.0157 - mcc: 0.6103 - val_loss: 0.9123 - val_accuracy: 0.6665 - val_recall: 0.5628 - val_precision: 0.7683 - val_AUROC: 0.9526 - val_AUPRC: 0.7545 - val_f1_score: 0.6497 - val_balanced_accuracy: 0.7720 - val_specificity: 0.9811 - val_miss_rate: 0.4372 - val_fall_out: 0.0189 - val_mcc: 0.6263
Epoch 9/100
63/63 [==============================] - 3s 50ms/step - loss: 0.8599 - accuracy: 0.7118 - recall: 0.5924 - precision: 0.8134 - AUROC: 0.9571 - AUPRC: 0.7912 - f1_score: 0.6856 - balanced_accuracy: 0.7887 - specificity: 0.9849 - miss_rate: 0.4076 - fall_out: 0.0151 - mcc: 0.6665 - val_loss: 0.7983 - val_accuracy: 0.7216 - val_recall: 0.6289 - val_precision: 0.8182 - val_AUROC: 0.9637 - val_AUPRC: 0.8149 - val_f1_score: 0.7112 - val_balanced_accuracy: 0.8067 - val_specificity: 0.9845 - val_miss_rate: 0.3711 - val_fall_out: 0.0155 - val_mcc: 0.6908
Epoch 10/100
63/63 [==============================] - 3s 50ms/step - loss: 0.7949 - accuracy: 0.7261 - recall: 0.6264 - precision: 0.8206 - AUROC: 0.9632 - AUPRC: 0.8117 - f1_score: 0.7105 - balanced_accuracy: 0.8056 - specificity: 0.9848 - miss_rate: 0.3736 - fall_out: 0.0152 - mcc: 0.6905 - val_loss: 0.8200 - val_accuracy: 0.7216 - val_recall: 0.6370 - val_precision: 0.8020 - val_AUROC: 0.9610 - val_AUPRC: 0.8087 - val_f1_score: 0.7100 - val_balanced_accuracy: 0.8097 - val_specificity: 0.9825 - val_miss_rate: 0.3630 - val_fall_out: 0.0175 - val_mcc: 0.6873
Epoch 11/100
63/63 [==============================] - 3s 50ms/step - loss: 0.7250 - accuracy: 0.7610 - recall: 0.6698 - precision: 0.8410 - AUROC: 0.9689 - AUPRC: 0.8406 - f1_score: 0.7457 - balanced_accuracy: 0.8279 - specificity: 0.9859 - miss_rate: 0.3302 - fall_out: 0.0141 - mcc: 0.7266 - val_loss: 0.7841 - val_accuracy: 0.7316 - val_recall: 0.6510 - val_precision: 0.8270 - val_AUROC: 0.9636 - val_AUPRC: 0.8232 - val_f1_score: 0.7285 - val_balanced_accuracy: 0.8179 - val_specificity: 0.9849 - val_miss_rate: 0.3490 - val_fall_out: 0.0151 - val_mcc: 0.7083
Epoch 12/100
63/63 [==============================] - 3s 50ms/step - loss: 0.6811 - accuracy: 0.7758 - recall: 0.6901 - precision: 0.8539 - AUROC: 0.9723 - AUPRC: 0.8566 - f1_score: 0.7633 - balanced_accuracy: 0.8385 - specificity: 0.9869 - miss_rate: 0.3099 - fall_out: 0.0131 - mcc: 0.7452 - val_loss: 0.6620 - val_accuracy: 0.7807 - val_recall: 0.7246 - val_precision: 0.8542 - val_AUROC: 0.9735 - val_AUPRC: 0.8650 - val_f1_score: 0.7841 - val_balanced_accuracy: 0.8554 - val_specificity: 0.9863 - val_miss_rate: 0.2754 - val_fall_out: 0.0137 - val_mcc: 0.7654
Epoch 13/100
63/63 [==============================] - 3s 50ms/step - loss: 0.5900 - accuracy: 0.8056 - recall: 0.7365 - precision: 0.8746 - AUROC: 0.9784 - AUPRC: 0.8865 - f1_score: 0.7996 - balanced_accuracy: 0.8624 - specificity: 0.9883 - miss_rate: 0.2635 - fall_out: 0.0117 - mcc: 0.7829 - val_loss: 0.6478 - val_accuracy: 0.7842 - val_recall: 0.7421 - val_precision: 0.8378 - val_AUROC: 0.9756 - val_AUPRC: 0.8715 - val_f1_score: 0.7870 - val_balanced_accuracy: 0.8631 - val_specificity: 0.9840 - val_miss_rate: 0.2579 - val_fall_out: 0.0160 - val_mcc: 0.7667
Epoch 14/100
63/63 [==============================] - 3s 49ms/step - loss: 0.5285 - accuracy: 0.8233 - recall: 0.7677 - precision: 0.8795 - AUROC: 0.9826 - AUPRC: 0.9050 - f1_score: 0.8198 - balanced_accuracy: 0.8780 - specificity: 0.9883 - miss_rate: 0.2323 - fall_out: 0.0117 - mcc: 0.8035 - val_loss: 0.6124 - val_accuracy: 0.7822 - val_recall: 0.7386 - val_precision: 0.8453 - val_AUROC: 0.9777 - val_AUPRC: 0.8823 - val_f1_score: 0.7883 - val_balanced_accuracy: 0.8618 - val_specificity: 0.9850 - val_miss_rate: 0.2614 - val_fall_out: 0.0150 - val_mcc: 0.7687
Epoch 15/100
63/63 [==============================] - 3s 50ms/step - loss: 0.5211 - accuracy: 0.8245 - recall: 0.7643 - precision: 0.8833 - AUROC: 0.9828 - AUPRC: 0.9078 - f1_score: 0.8195 - balanced_accuracy: 0.8765 - specificity: 0.9888 - miss_rate: 0.2357 - fall_out: 0.0112 - mcc: 0.8036 - val_loss: 0.6289 - val_accuracy: 0.7897 - val_recall: 0.7581 - val_precision: 0.8296 - val_AUROC: 0.9748 - val_AUPRC: 0.8814 - val_f1_score: 0.7923 - val_balanced_accuracy: 0.8704 - val_specificity: 0.9827 - val_miss_rate: 0.2419 - val_fall_out: 0.0173 - val_mcc: 0.7713
Epoch 16/100
63/63 [==============================] - 3s 50ms/step - loss: 0.4250 - accuracy: 0.8580 - recall: 0.8149 - precision: 0.9004 - AUROC: 0.9880 - AUPRC: 0.9332 - f1_score: 0.8555 - balanced_accuracy: 0.9024 - specificity: 0.9900 - miss_rate: 0.1851 - fall_out: 0.0100 - mcc: 0.8416 - val_loss: 0.5518 - val_accuracy: 0.8222 - val_recall: 0.7872 - val_precision: 0.8728 - val_AUROC: 0.9792 - val_AUPRC: 0.9025 - val_f1_score: 0.8278 - val_balanced_accuracy: 0.8872 - val_specificity: 0.9873 - val_miss_rate: 0.2128 - val_fall_out: 0.0127 - val_mcc: 0.8111
Epoch 17/100
63/63 [==============================] - 3s 50ms/step - loss: 0.3655 - accuracy: 0.8785 - recall: 0.8448 - precision: 0.9094 - AUROC: 0.9907 - AUPRC: 0.9469 - f1_score: 0.8759 - balanced_accuracy: 0.9177 - specificity: 0.9906 - miss_rate: 0.1552 - fall_out: 0.0094 - mcc: 0.8634 - val_loss: 0.5840 - val_accuracy: 0.7987 - val_recall: 0.7661 - val_precision: 0.8444 - val_AUROC: 0.9786 - val_AUPRC: 0.8929 - val_f1_score: 0.8034 - val_balanced_accuracy: 0.8752 - val_specificity: 0.9843 - val_miss_rate: 0.2339 - val_fall_out: 0.0157 - val_mcc: 0.7838
Epoch 18/100
63/63 [==============================] - 3s 50ms/step - loss: 0.3417 - accuracy: 0.8885 - recall: 0.8592 - precision: 0.9187 - AUROC: 0.9915 - AUPRC: 0.9537 - f1_score: 0.8880 - balanced_accuracy: 0.9254 - specificity: 0.9916 - miss_rate: 0.1408 - fall_out: 0.0084 - mcc: 0.8766 - val_loss: 0.6250 - val_accuracy: 0.8042 - val_recall: 0.7867 - val_precision: 0.8352 - val_AUROC: 0.9754 - val_AUPRC: 0.8881 - val_f1_score: 0.8102 - val_balanced_accuracy: 0.8847 - val_specificity: 0.9828 - val_miss_rate: 0.2133 - val_fall_out: 0.0172 - val_mcc: 0.7903
250/250 [==============================] - 2s 7ms/step - loss: 0.2476 - accuracy: 0.9124 - recall: 0.8932 - precision: 0.9291 - AUROC: 0.9958 - AUPRC: 0.9716 - f1_score: 0.9108 - balanced_accuracy: 0.9428 - specificity: 0.9924 - miss_rate: 0.1068 - fall_out: 0.0076 - mcc: 0.9013
63/63 [==============================] - 0s 7ms/step - loss: 0.6250 - accuracy: 0.8042 - recall: 0.7867 - precision: 0.8352 - AUROC: 0.9754 - AUPRC: 0.8881 - f1_score: 0.8102 - balanced_accuracy: 0.8847 - specificity: 0.9828 - miss_rate: 0.2133 - fall_out: 0.0172 - mcc: 0.7903
4it [03:16, 51.95s/it]
-- HOLDOUT 5
Model: "CNN_Mfccs_3s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_320 (Conv2D) (None, 20, 130, 256) 2560
max_pooling2d_320 (MaxPooli (None, 10, 65, 256) 0
ng2D)
conv2d_321 (Conv2D) (None, 10, 65, 256) 590080
max_pooling2d_321 (MaxPooli (None, 5, 33, 256) 0
ng2D)
conv2d_322 (Conv2D) (None, 5, 33, 256) 590080
max_pooling2d_322 (MaxPooli (None, 3, 17, 256) 0
ng2D)
conv2d_323 (Conv2D) (None, 3, 17, 512) 1180160
max_pooling2d_323 (MaxPooli (None, 2, 9, 512) 0
ng2D)
flatten_80 (Flatten) (None, 9216) 0
dense_240 (Dense) (None, 256) 2359552
dropout_358 (Dropout) (None, 256) 0
dense_241 (Dense) (None, 256) 65792
dropout_359 (Dropout) (None, 256) 0
dense_242 (Dense) (None, 10) 2570
=================================================================
Total params: 4,790,794
Trainable params: 4,790,794
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 5s 57ms/step - loss: 2.0743 - accuracy: 0.2261 - recall: 0.0531 - precision: 0.6181 - AUROC: 0.6894 - AUPRC: 0.2373 - f1_score: 0.0978 - balanced_accuracy: 0.5247 - specificity: 0.9964 - miss_rate: 0.9469 - fall_out: 0.0036 - mcc: 0.1608 - val_loss: 1.6645 - val_accuracy: 0.3871 - val_recall: 0.1467 - val_precision: 0.7670 - val_AUROC: 0.8377 - val_AUPRC: 0.4332 - val_f1_score: 0.2463 - val_balanced_accuracy: 0.5709 - val_specificity: 0.9950 - val_miss_rate: 0.8533 - val_fall_out: 0.0050 - val_mcc: 0.3105
Epoch 2/100
63/63 [==============================] - 3s 50ms/step - loss: 1.7106 - accuracy: 0.3577 - recall: 0.1554 - precision: 0.7035 - AUROC: 0.8186 - AUPRC: 0.3976 - f1_score: 0.2546 - balanced_accuracy: 0.5741 - specificity: 0.9927 - miss_rate: 0.8446 - fall_out: 0.0073 - mcc: 0.3024 - val_loss: 1.4992 - val_accuracy: 0.4382 - val_recall: 0.1768 - val_precision: 0.7724 - val_AUROC: 0.8705 - val_AUPRC: 0.4851 - val_f1_score: 0.2877 - val_balanced_accuracy: 0.5855 - val_specificity: 0.9942 - val_miss_rate: 0.8232 - val_fall_out: 0.0058 - val_mcc: 0.3430
Epoch 3/100
63/63 [==============================] - 3s 50ms/step - loss: 1.5588 - accuracy: 0.4084 - recall: 0.2013 - precision: 0.7155 - AUROC: 0.8555 - AUPRC: 0.4610 - f1_score: 0.3142 - balanced_accuracy: 0.5962 - specificity: 0.9911 - miss_rate: 0.7987 - fall_out: 0.0089 - mcc: 0.3491 - val_loss: 1.6838 - val_accuracy: 0.3575 - val_recall: 0.1557 - val_precision: 0.7913 - val_AUROC: 0.8230 - val_AUPRC: 0.4263 - val_f1_score: 0.2603 - val_balanced_accuracy: 0.5756 - val_specificity: 0.9954 - val_miss_rate: 0.8443 - val_fall_out: 0.0046 - val_mcc: 0.3265
Epoch 4/100
63/63 [==============================] - 3s 50ms/step - loss: 1.4557 - accuracy: 0.4448 - recall: 0.2400 - precision: 0.7352 - AUROC: 0.8763 - AUPRC: 0.5106 - f1_score: 0.3619 - balanced_accuracy: 0.6152 - specificity: 0.9904 - miss_rate: 0.7600 - fall_out: 0.0096 - mcc: 0.3889 - val_loss: 1.3446 - val_accuracy: 0.4922 - val_recall: 0.2909 - val_precision: 0.6941 - val_AUROC: 0.8964 - val_AUPRC: 0.5570 - val_f1_score: 0.4100 - val_balanced_accuracy: 0.6383 - val_specificity: 0.9858 - val_miss_rate: 0.7091 - val_fall_out: 0.0142 - val_mcc: 0.4142
Epoch 5/100
63/63 [==============================] - 3s 50ms/step - loss: 1.3594 - accuracy: 0.4924 - recall: 0.2779 - precision: 0.7362 - AUROC: 0.8937 - AUPRC: 0.5569 - f1_score: 0.4035 - balanced_accuracy: 0.6334 - specificity: 0.9889 - miss_rate: 0.7221 - fall_out: 0.0111 - mcc: 0.4201 - val_loss: 1.2437 - val_accuracy: 0.5303 - val_recall: 0.3425 - val_precision: 0.7435 - val_AUROC: 0.9115 - val_AUPRC: 0.6106 - val_f1_score: 0.4690 - val_balanced_accuracy: 0.6647 - val_specificity: 0.9869 - val_miss_rate: 0.6575 - val_fall_out: 0.0131 - val_mcc: 0.4714
Epoch 6/100
63/63 [==============================] - 3s 50ms/step - loss: 1.2622 - accuracy: 0.5257 - recall: 0.3327 - precision: 0.7390 - AUROC: 0.9098 - AUPRC: 0.5980 - f1_score: 0.4588 - balanced_accuracy: 0.6598 - specificity: 0.9869 - miss_rate: 0.6673 - fall_out: 0.0131 - mcc: 0.4625 - val_loss: 1.1593 - val_accuracy: 0.5653 - val_recall: 0.3380 - val_precision: 0.8113 - val_AUROC: 0.9249 - val_AUPRC: 0.6480 - val_f1_score: 0.4772 - val_balanced_accuracy: 0.6646 - val_specificity: 0.9913 - val_miss_rate: 0.6620 - val_fall_out: 0.0087 - val_mcc: 0.4944
Epoch 7/100
63/63 [==============================] - 3s 50ms/step - loss: 1.1624 - accuracy: 0.5685 - recall: 0.3821 - precision: 0.7548 - AUROC: 0.9237 - AUPRC: 0.6455 - f1_score: 0.5074 - balanced_accuracy: 0.6842 - specificity: 0.9862 - miss_rate: 0.6179 - fall_out: 0.0138 - mcc: 0.5040 - val_loss: 1.0599 - val_accuracy: 0.6269 - val_recall: 0.3861 - val_precision: 0.8426 - val_AUROC: 0.9386 - val_AUPRC: 0.7023 - val_f1_score: 0.5295 - val_balanced_accuracy: 0.6890 - val_specificity: 0.9920 - val_miss_rate: 0.6139 - val_fall_out: 0.0080 - val_mcc: 0.5424
Epoch 8/100
63/63 [==============================] - 3s 49ms/step - loss: 1.0829 - accuracy: 0.6053 - recall: 0.4326 - precision: 0.7578 - AUROC: 0.9341 - AUPRC: 0.6814 - f1_score: 0.5508 - balanced_accuracy: 0.7086 - specificity: 0.9846 - miss_rate: 0.5674 - fall_out: 0.0154 - mcc: 0.5395 - val_loss: 1.0363 - val_accuracy: 0.6360 - val_recall: 0.4817 - val_precision: 0.7581 - val_AUROC: 0.9394 - val_AUPRC: 0.7094 - val_f1_score: 0.5891 - val_balanced_accuracy: 0.7323 - val_specificity: 0.9829 - val_miss_rate: 0.5183 - val_fall_out: 0.0171 - val_mcc: 0.5714
Epoch 9/100
63/63 [==============================] - 3s 50ms/step - loss: 1.0318 - accuracy: 0.6303 - recall: 0.4758 - precision: 0.7652 - AUROC: 0.9397 - AUPRC: 0.7101 - f1_score: 0.5868 - balanced_accuracy: 0.7298 - specificity: 0.9838 - miss_rate: 0.5242 - fall_out: 0.0162 - mcc: 0.5709 - val_loss: 1.0051 - val_accuracy: 0.6630 - val_recall: 0.4597 - val_precision: 0.8102 - val_AUROC: 0.9444 - val_AUPRC: 0.7278 - val_f1_score: 0.5866 - val_balanced_accuracy: 0.7239 - val_specificity: 0.9880 - val_miss_rate: 0.5403 - val_fall_out: 0.0120 - val_mcc: 0.5806
Epoch 10/100
63/63 [==============================] - 3s 50ms/step - loss: 0.9183 - accuracy: 0.6766 - recall: 0.5392 - precision: 0.7949 - AUROC: 0.9520 - AUPRC: 0.7584 - f1_score: 0.6425 - balanced_accuracy: 0.7619 - specificity: 0.9845 - miss_rate: 0.4608 - fall_out: 0.0155 - mcc: 0.6248 - val_loss: 0.9479 - val_accuracy: 0.6715 - val_recall: 0.5588 - val_precision: 0.7788 - val_AUROC: 0.9488 - val_AUPRC: 0.7501 - val_f1_score: 0.6507 - val_balanced_accuracy: 0.7706 - val_specificity: 0.9824 - val_miss_rate: 0.4412 - val_fall_out: 0.0176 - val_mcc: 0.6291
Epoch 11/100
63/63 [==============================] - 3s 50ms/step - loss: 0.8712 - accuracy: 0.6961 - recall: 0.5758 - precision: 0.8072 - AUROC: 0.9566 - AUPRC: 0.7787 - f1_score: 0.6721 - balanced_accuracy: 0.7802 - specificity: 0.9847 - miss_rate: 0.4242 - fall_out: 0.0153 - mcc: 0.6533 - val_loss: 0.9157 - val_accuracy: 0.6785 - val_recall: 0.5984 - val_precision: 0.7597 - val_AUROC: 0.9517 - val_AUPRC: 0.7659 - val_f1_score: 0.6695 - val_balanced_accuracy: 0.7887 - val_specificity: 0.9790 - val_miss_rate: 0.4016 - val_fall_out: 0.0210 - val_mcc: 0.6430
Epoch 12/100
63/63 [==============================] - 3s 50ms/step - loss: 0.7690 - accuracy: 0.7365 - recall: 0.6354 - precision: 0.8257 - AUROC: 0.9655 - AUPRC: 0.8199 - f1_score: 0.7181 - balanced_accuracy: 0.8102 - specificity: 0.9851 - miss_rate: 0.3646 - fall_out: 0.0149 - mcc: 0.6984 - val_loss: 0.8051 - val_accuracy: 0.7211 - val_recall: 0.6450 - val_precision: 0.7936 - val_AUROC: 0.9628 - val_AUPRC: 0.8062 - val_f1_score: 0.7116 - val_balanced_accuracy: 0.8132 - val_specificity: 0.9814 - val_miss_rate: 0.3550 - val_fall_out: 0.0186 - val_mcc: 0.6876
Epoch 13/100
63/63 [==============================] - 3s 50ms/step - loss: 0.7363 - accuracy: 0.7496 - recall: 0.6589 - precision: 0.8338 - AUROC: 0.9683 - AUPRC: 0.8358 - f1_score: 0.7361 - balanced_accuracy: 0.8222 - specificity: 0.9854 - miss_rate: 0.3411 - fall_out: 0.0146 - mcc: 0.7165 - val_loss: 0.8219 - val_accuracy: 0.7261 - val_recall: 0.6335 - val_precision: 0.8214 - val_AUROC: 0.9600 - val_AUPRC: 0.8060 - val_f1_score: 0.7153 - val_balanced_accuracy: 0.8091 - val_specificity: 0.9847 - val_miss_rate: 0.3665 - val_fall_out: 0.0153 - val_mcc: 0.6951
Epoch 14/100
63/63 [==============================] - 3s 50ms/step - loss: 0.7197 - accuracy: 0.7554 - recall: 0.6640 - precision: 0.8476 - AUROC: 0.9697 - AUPRC: 0.8422 - f1_score: 0.7446 - balanced_accuracy: 0.8253 - specificity: 0.9867 - miss_rate: 0.3360 - fall_out: 0.0133 - mcc: 0.7265 - val_loss: 0.7107 - val_accuracy: 0.7677 - val_recall: 0.7006 - val_precision: 0.8557 - val_AUROC: 0.9689 - val_AUPRC: 0.8481 - val_f1_score: 0.7704 - val_balanced_accuracy: 0.8437 - val_specificity: 0.9869 - val_miss_rate: 0.2994 - val_fall_out: 0.0131 - val_mcc: 0.7522
Epoch 15/100
63/63 [==============================] - 3s 54ms/step - loss: 0.6052 - accuracy: 0.7972 - recall: 0.7271 - precision: 0.8672 - AUROC: 0.9777 - AUPRC: 0.8810 - f1_score: 0.7910 - balanced_accuracy: 0.8574 - specificity: 0.9876 - miss_rate: 0.2729 - fall_out: 0.0124 - mcc: 0.7736 - val_loss: 0.7284 - val_accuracy: 0.7631 - val_recall: 0.7061 - val_precision: 0.8319 - val_AUROC: 0.9671 - val_AUPRC: 0.8443 - val_f1_score: 0.7638 - val_balanced_accuracy: 0.8451 - val_specificity: 0.9841 - val_miss_rate: 0.2939 - val_fall_out: 0.0159 - val_mcc: 0.7430
Epoch 16/100
63/63 [==============================] - 3s 50ms/step - loss: 0.5361 - accuracy: 0.8211 - recall: 0.7625 - precision: 0.8809 - AUROC: 0.9817 - AUPRC: 0.9012 - f1_score: 0.8175 - balanced_accuracy: 0.8755 - specificity: 0.9885 - miss_rate: 0.2375 - fall_out: 0.0115 - mcc: 0.8013 - val_loss: 0.7657 - val_accuracy: 0.7491 - val_recall: 0.6890 - val_precision: 0.8118 - val_AUROC: 0.9647 - val_AUPRC: 0.8297 - val_f1_score: 0.7454 - val_balanced_accuracy: 0.8356 - val_specificity: 0.9823 - val_miss_rate: 0.3110 - val_fall_out: 0.0177 - val_mcc: 0.7226
250/250 [==============================] - 2s 7ms/step - loss: 0.4873 - accuracy: 0.8313 - recall: 0.7688 - precision: 0.8804 - AUROC: 0.9859 - AUPRC: 0.9136 - f1_score: 0.8208 - balanced_accuracy: 0.8786 - specificity: 0.9884 - miss_rate: 0.2312 - fall_out: 0.0116 - mcc: 0.8046
63/63 [==============================] - 0s 7ms/step - loss: 0.7657 - accuracy: 0.7491 - recall: 0.6890 - precision: 0.8118 - AUROC: 0.9647 - AUPRC: 0.8297 - f1_score: 0.7454 - balanced_accuracy: 0.8356 - specificity: 0.9823 - miss_rate: 0.3110 - fall_out: 0.0177 - mcc: 0.7226
5it [04:11, 52.84s/it]
-- HOLDOUT 6
Model: "CNN_Mfccs_3s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_324 (Conv2D) (None, 20, 130, 256) 2560
max_pooling2d_324 (MaxPooli (None, 10, 65, 256) 0
ng2D)
conv2d_325 (Conv2D) (None, 10, 65, 256) 590080
max_pooling2d_325 (MaxPooli (None, 5, 33, 256) 0
ng2D)
conv2d_326 (Conv2D) (None, 5, 33, 256) 590080
max_pooling2d_326 (MaxPooli (None, 3, 17, 256) 0
ng2D)
conv2d_327 (Conv2D) (None, 3, 17, 512) 1180160
max_pooling2d_327 (MaxPooli (None, 2, 9, 512) 0
ng2D)
flatten_81 (Flatten) (None, 9216) 0
dense_243 (Dense) (None, 256) 2359552
dropout_360 (Dropout) (None, 256) 0
dense_244 (Dense) (None, 256) 65792
dropout_361 (Dropout) (None, 256) 0
dense_245 (Dense) (None, 10) 2570
=================================================================
Total params: 4,790,794
Trainable params: 4,790,794
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 5s 57ms/step - loss: 2.0399 - accuracy: 0.2401 - recall: 0.0599 - precision: 0.5968 - AUROC: 0.7082 - AUPRC: 0.2523 - f1_score: 0.1088 - balanced_accuracy: 0.5277 - specificity: 0.9955 - miss_rate: 0.9401 - fall_out: 0.0045 - mcc: 0.1667 - val_loss: 1.6727 - val_accuracy: 0.3430 - val_recall: 0.1888 - val_precision: 0.6905 - val_AUROC: 0.8332 - val_AUPRC: 0.4214 - val_f1_score: 0.2965 - val_balanced_accuracy: 0.5897 - val_specificity: 0.9906 - val_miss_rate: 0.8112 - val_fall_out: 0.0094 - val_mcc: 0.3300
Epoch 2/100
63/63 [==============================] - 3s 50ms/step - loss: 1.6716 - accuracy: 0.3702 - recall: 0.1740 - precision: 0.7015 - AUROC: 0.8272 - AUPRC: 0.4140 - f1_score: 0.2788 - balanced_accuracy: 0.5829 - specificity: 0.9918 - miss_rate: 0.8260 - fall_out: 0.0082 - mcc: 0.3197 - val_loss: 1.4842 - val_accuracy: 0.4296 - val_recall: 0.2419 - val_precision: 0.7679 - val_AUROC: 0.8714 - val_AUPRC: 0.5074 - val_f1_score: 0.3679 - val_balanced_accuracy: 0.6169 - val_specificity: 0.9919 - val_miss_rate: 0.7581 - val_fall_out: 0.0081 - val_mcc: 0.4015
Epoch 3/100
63/63 [==============================] - 3s 50ms/step - loss: 1.4722 - accuracy: 0.4446 - recall: 0.2357 - precision: 0.7363 - AUROC: 0.8729 - AUPRC: 0.5005 - f1_score: 0.3571 - balanced_accuracy: 0.6132 - specificity: 0.9906 - miss_rate: 0.7643 - fall_out: 0.0094 - mcc: 0.3857 - val_loss: 1.3695 - val_accuracy: 0.4912 - val_recall: 0.2479 - val_precision: 0.7489 - val_AUROC: 0.8920 - val_AUPRC: 0.5540 - val_f1_score: 0.3725 - val_balanced_accuracy: 0.6193 - val_specificity: 0.9908 - val_miss_rate: 0.7521 - val_fall_out: 0.0092 - val_mcc: 0.4002
Epoch 4/100
63/63 [==============================] - 3s 50ms/step - loss: 1.3360 - accuracy: 0.5036 - recall: 0.2869 - precision: 0.7241 - AUROC: 0.8977 - AUPRC: 0.5607 - f1_score: 0.4110 - balanced_accuracy: 0.6374 - specificity: 0.9879 - miss_rate: 0.7131 - fall_out: 0.0121 - mcc: 0.4226 - val_loss: 1.3200 - val_accuracy: 0.5273 - val_recall: 0.3385 - val_precision: 0.7613 - val_AUROC: 0.8995 - val_AUPRC: 0.5992 - val_f1_score: 0.4686 - val_balanced_accuracy: 0.6634 - val_specificity: 0.9882 - val_miss_rate: 0.6615 - val_fall_out: 0.0118 - val_mcc: 0.4755
Epoch 5/100
63/63 [==============================] - 3s 50ms/step - loss: 1.2529 - accuracy: 0.5411 - recall: 0.3407 - precision: 0.7238 - AUROC: 0.9112 - AUPRC: 0.6004 - f1_score: 0.4633 - balanced_accuracy: 0.6631 - specificity: 0.9856 - miss_rate: 0.6593 - fall_out: 0.0144 - mcc: 0.4621 - val_loss: 1.1110 - val_accuracy: 0.5779 - val_recall: 0.3871 - val_precision: 0.7864 - val_AUROC: 0.9324 - val_AUPRC: 0.6744 - val_f1_score: 0.5188 - val_balanced_accuracy: 0.6877 - val_specificity: 0.9883 - val_miss_rate: 0.6129 - val_fall_out: 0.0117 - val_mcc: 0.5206
Epoch 6/100
63/63 [==============================] - 3s 50ms/step - loss: 1.1180 - accuracy: 0.5979 - recall: 0.4223 - precision: 0.7440 - AUROC: 0.9297 - AUPRC: 0.6670 - f1_score: 0.5388 - balanced_accuracy: 0.7031 - specificity: 0.9839 - miss_rate: 0.5777 - fall_out: 0.0161 - mcc: 0.5266 - val_loss: 1.0126 - val_accuracy: 0.6575 - val_recall: 0.4537 - val_precision: 0.8170 - val_AUROC: 0.9427 - val_AUPRC: 0.7268 - val_f1_score: 0.5834 - val_balanced_accuracy: 0.7212 - val_specificity: 0.9887 - val_miss_rate: 0.5463 - val_fall_out: 0.0113 - val_mcc: 0.5795
Epoch 7/100
63/63 [==============================] - 3s 50ms/step - loss: 1.0190 - accuracy: 0.6379 - recall: 0.4832 - precision: 0.7716 - AUROC: 0.9414 - AUPRC: 0.7145 - f1_score: 0.5943 - balanced_accuracy: 0.7337 - specificity: 0.9841 - miss_rate: 0.5168 - fall_out: 0.0159 - mcc: 0.5786 - val_loss: 0.9100 - val_accuracy: 0.6780 - val_recall: 0.5638 - val_precision: 0.7963 - val_AUROC: 0.9530 - val_AUPRC: 0.7647 - val_f1_score: 0.6602 - val_balanced_accuracy: 0.7739 - val_specificity: 0.9840 - val_miss_rate: 0.4362 - val_fall_out: 0.0160 - val_mcc: 0.6407
Epoch 8/100
63/63 [==============================] - 3s 50ms/step - loss: 0.9557 - accuracy: 0.6681 - recall: 0.5358 - precision: 0.7909 - AUROC: 0.9478 - AUPRC: 0.7432 - f1_score: 0.6388 - balanced_accuracy: 0.7600 - specificity: 0.9843 - miss_rate: 0.4642 - fall_out: 0.0157 - mcc: 0.6208 - val_loss: 0.9400 - val_accuracy: 0.6645 - val_recall: 0.5704 - val_precision: 0.7629 - val_AUROC: 0.9493 - val_AUPRC: 0.7520 - val_f1_score: 0.6527 - val_balanced_accuracy: 0.7753 - val_specificity: 0.9803 - val_miss_rate: 0.4296 - val_fall_out: 0.0197 - val_mcc: 0.6281
Epoch 9/100
63/63 [==============================] - 3s 50ms/step - loss: 0.8508 - accuracy: 0.7065 - recall: 0.5986 - precision: 0.8122 - AUROC: 0.9583 - AUPRC: 0.7904 - f1_score: 0.6892 - balanced_accuracy: 0.7916 - specificity: 0.9846 - miss_rate: 0.4014 - fall_out: 0.0154 - mcc: 0.6696 - val_loss: 0.8085 - val_accuracy: 0.7251 - val_recall: 0.6360 - val_precision: 0.8115 - val_AUROC: 0.9619 - val_AUPRC: 0.8100 - val_f1_score: 0.7131 - val_balanced_accuracy: 0.8098 - val_specificity: 0.9836 - val_miss_rate: 0.3640 - val_fall_out: 0.0164 - val_mcc: 0.6916
Epoch 10/100
63/63 [==============================] - 3s 50ms/step - loss: 0.7544 - accuracy: 0.7381 - recall: 0.6478 - precision: 0.8300 - AUROC: 0.9670 - AUPRC: 0.8261 - f1_score: 0.7277 - balanced_accuracy: 0.8165 - specificity: 0.9853 - miss_rate: 0.3522 - fall_out: 0.0147 - mcc: 0.7080 - val_loss: 0.7539 - val_accuracy: 0.7381 - val_recall: 0.6550 - val_precision: 0.8242 - val_AUROC: 0.9667 - val_AUPRC: 0.8268 - val_f1_score: 0.7299 - val_balanced_accuracy: 0.8197 - val_specificity: 0.9845 - val_miss_rate: 0.3450 - val_fall_out: 0.0155 - val_mcc: 0.7093
Epoch 11/100
63/63 [==============================] - 3s 50ms/step - loss: 0.7050 - accuracy: 0.7604 - recall: 0.6740 - precision: 0.8454 - AUROC: 0.9709 - AUPRC: 0.8466 - f1_score: 0.7500 - balanced_accuracy: 0.8301 - specificity: 0.9863 - miss_rate: 0.3260 - fall_out: 0.0137 - mcc: 0.7313 - val_loss: 0.7538 - val_accuracy: 0.7376 - val_recall: 0.6715 - val_precision: 0.8217 - val_AUROC: 0.9660 - val_AUPRC: 0.8319 - val_f1_score: 0.7390 - val_balanced_accuracy: 0.8277 - val_specificity: 0.9838 - val_miss_rate: 0.3285 - val_fall_out: 0.0162 - val_mcc: 0.7177
Epoch 12/100
63/63 [==============================] - 3s 54ms/step - loss: 0.6096 - accuracy: 0.7945 - recall: 0.7233 - precision: 0.8643 - AUROC: 0.9777 - AUPRC: 0.8800 - f1_score: 0.7875 - balanced_accuracy: 0.8553 - specificity: 0.9874 - miss_rate: 0.2767 - fall_out: 0.0126 - mcc: 0.7699 - val_loss: 0.6340 - val_accuracy: 0.7782 - val_recall: 0.7236 - val_precision: 0.8431 - val_AUROC: 0.9772 - val_AUPRC: 0.8756 - val_f1_score: 0.7788 - val_balanced_accuracy: 0.8543 - val_specificity: 0.9850 - val_miss_rate: 0.2764 - val_fall_out: 0.0150 - val_mcc: 0.7589
Epoch 13/100
63/63 [==============================] - 3s 52ms/step - loss: 0.5478 - accuracy: 0.8143 - recall: 0.7450 - precision: 0.8706 - AUROC: 0.9820 - AUPRC: 0.8983 - f1_score: 0.8029 - balanced_accuracy: 0.8663 - specificity: 0.9877 - miss_rate: 0.2550 - fall_out: 0.0123 - mcc: 0.7858 - val_loss: 0.6264 - val_accuracy: 0.7927 - val_recall: 0.7496 - val_precision: 0.8477 - val_AUROC: 0.9764 - val_AUPRC: 0.8787 - val_f1_score: 0.7956 - val_balanced_accuracy: 0.8673 - val_specificity: 0.9850 - val_miss_rate: 0.2504 - val_fall_out: 0.0150 - val_mcc: 0.7763
Epoch 14/100
63/63 [==============================] - 3s 52ms/step - loss: 0.5113 - accuracy: 0.8283 - recall: 0.7730 - precision: 0.8825 - AUROC: 0.9836 - AUPRC: 0.9097 - f1_score: 0.8241 - balanced_accuracy: 0.8808 - specificity: 0.9886 - miss_rate: 0.2270 - fall_out: 0.0114 - mcc: 0.8082 - val_loss: 0.6637 - val_accuracy: 0.7847 - val_recall: 0.7321 - val_precision: 0.8417 - val_AUROC: 0.9732 - val_AUPRC: 0.8666 - val_f1_score: 0.7831 - val_balanced_accuracy: 0.8584 - val_specificity: 0.9847 - val_miss_rate: 0.2679 - val_fall_out: 0.0153 - val_mcc: 0.7631
Epoch 15/100
63/63 [==============================] - 3s 50ms/step - loss: 0.4855 - accuracy: 0.8383 - recall: 0.7838 - precision: 0.8875 - AUROC: 0.9851 - AUPRC: 0.9178 - f1_score: 0.8325 - balanced_accuracy: 0.8864 - specificity: 0.9890 - miss_rate: 0.2162 - fall_out: 0.0110 - mcc: 0.8170 - val_loss: 0.6556 - val_accuracy: 0.7887 - val_recall: 0.7481 - val_precision: 0.8474 - val_AUROC: 0.9740 - val_AUPRC: 0.8740 - val_f1_score: 0.7947 - val_balanced_accuracy: 0.8666 - val_specificity: 0.9850 - val_miss_rate: 0.2519 - val_fall_out: 0.0150 - val_mcc: 0.7753
250/250 [==============================] - 2s 8ms/step - loss: 0.3337 - accuracy: 0.8844 - recall: 0.8468 - precision: 0.9255 - AUROC: 0.9933 - AUPRC: 0.9574 - f1_score: 0.8844 - balanced_accuracy: 0.9196 - specificity: 0.9924 - miss_rate: 0.1532 - fall_out: 0.0076 - mcc: 0.8733
63/63 [==============================] - 0s 8ms/step - loss: 0.6556 - accuracy: 0.7887 - recall: 0.7481 - precision: 0.8474 - AUROC: 0.9740 - AUPRC: 0.8740 - f1_score: 0.7947 - balanced_accuracy: 0.8666 - specificity: 0.9850 - miss_rate: 0.2519 - fall_out: 0.0150 - mcc: 0.7753
6it [05:03, 52.52s/it]
-- HOLDOUT 7
Model: "CNN_Mfccs_3s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_328 (Conv2D) (None, 20, 130, 256) 2560
max_pooling2d_328 (MaxPooli (None, 10, 65, 256) 0
ng2D)
conv2d_329 (Conv2D) (None, 10, 65, 256) 590080
max_pooling2d_329 (MaxPooli (None, 5, 33, 256) 0
ng2D)
conv2d_330 (Conv2D) (None, 5, 33, 256) 590080
max_pooling2d_330 (MaxPooli (None, 3, 17, 256) 0
ng2D)
conv2d_331 (Conv2D) (None, 3, 17, 512) 1180160
max_pooling2d_331 (MaxPooli (None, 2, 9, 512) 0
ng2D)
flatten_82 (Flatten) (None, 9216) 0
dense_246 (Dense) (None, 256) 2359552
dropout_362 (Dropout) (None, 256) 0
dense_247 (Dense) (None, 256) 65792
dropout_363 (Dropout) (None, 256) 0
dense_248 (Dense) (None, 10) 2570
=================================================================
Total params: 4,790,794
Trainable params: 4,790,794
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 5s 58ms/step - loss: 2.0920 - accuracy: 0.2044 - recall: 0.0416 - precision: 0.5866 - AUROC: 0.6813 - AUPRC: 0.2225 - f1_score: 0.0777 - balanced_accuracy: 0.5192 - specificity: 0.9967 - miss_rate: 0.9584 - fall_out: 0.0033 - mcc: 0.1370 - val_loss: 1.7222 - val_accuracy: 0.3275 - val_recall: 0.1768 - val_precision: 0.7792 - val_AUROC: 0.8163 - val_AUPRC: 0.4123 - val_f1_score: 0.2882 - val_balanced_accuracy: 0.5856 - val_specificity: 0.9944 - val_miss_rate: 0.8232 - val_fall_out: 0.0056 - val_mcc: 0.3449
Epoch 2/100
63/63 [==============================] - 3s 50ms/step - loss: 1.7573 - accuracy: 0.3446 - recall: 0.1374 - precision: 0.6759 - AUROC: 0.8077 - AUPRC: 0.3741 - f1_score: 0.2284 - balanced_accuracy: 0.5650 - specificity: 0.9927 - miss_rate: 0.8626 - fall_out: 0.0073 - mcc: 0.2765 - val_loss: 1.6205 - val_accuracy: 0.4036 - val_recall: 0.1652 - val_precision: 0.6358 - val_AUROC: 0.8473 - val_AUPRC: 0.4395 - val_f1_score: 0.2623 - val_balanced_accuracy: 0.5774 - val_specificity: 0.9895 - val_miss_rate: 0.8348 - val_fall_out: 0.0105 - val_mcc: 0.2918
Epoch 3/100
63/63 [==============================] - 3s 48ms/step - loss: 1.5540 - accuracy: 0.4232 - recall: 0.2012 - precision: 0.7081 - AUROC: 0.8570 - AUPRC: 0.4646 - f1_score: 0.3133 - balanced_accuracy: 0.5960 - specificity: 0.9908 - miss_rate: 0.7988 - fall_out: 0.0092 - mcc: 0.3466 - val_loss: 1.3577 - val_accuracy: 0.4747 - val_recall: 0.2439 - val_precision: 0.7515 - val_AUROC: 0.8933 - val_AUPRC: 0.5467 - val_f1_score: 0.3682 - val_balanced_accuracy: 0.6175 - val_specificity: 0.9910 - val_miss_rate: 0.7561 - val_fall_out: 0.0090 - val_mcc: 0.3977
Epoch 4/100
63/63 [==============================] - 3s 49ms/step - loss: 1.4386 - accuracy: 0.4624 - recall: 0.2508 - precision: 0.7246 - AUROC: 0.8801 - AUPRC: 0.5216 - f1_score: 0.3726 - balanced_accuracy: 0.6201 - specificity: 0.9894 - miss_rate: 0.7492 - fall_out: 0.0106 - mcc: 0.3942 - val_loss: 1.3164 - val_accuracy: 0.5028 - val_recall: 0.2554 - val_precision: 0.8121 - val_AUROC: 0.9016 - val_AUPRC: 0.5766 - val_f1_score: 0.3886 - val_balanced_accuracy: 0.6244 - val_specificity: 0.9934 - val_miss_rate: 0.7446 - val_fall_out: 0.0066 - val_mcc: 0.4277
Epoch 5/100
63/63 [==============================] - 3s 50ms/step - loss: 1.3285 - accuracy: 0.5085 - recall: 0.2933 - precision: 0.7278 - AUROC: 0.8994 - AUPRC: 0.5677 - f1_score: 0.4181 - balanced_accuracy: 0.6406 - specificity: 0.9878 - miss_rate: 0.7067 - fall_out: 0.0122 - mcc: 0.4288 - val_loss: 1.2520 - val_accuracy: 0.5373 - val_recall: 0.3165 - val_precision: 0.7670 - val_AUROC: 0.9127 - val_AUPRC: 0.6093 - val_f1_score: 0.4481 - val_balanced_accuracy: 0.6529 - val_specificity: 0.9893 - val_miss_rate: 0.6835 - val_fall_out: 0.0107 - val_mcc: 0.4612
Epoch 6/100
63/63 [==============================] - 3s 50ms/step - loss: 1.2537 - accuracy: 0.5342 - recall: 0.3397 - precision: 0.7457 - AUROC: 0.9110 - AUPRC: 0.6067 - f1_score: 0.4667 - balanced_accuracy: 0.6634 - specificity: 0.9871 - miss_rate: 0.6603 - fall_out: 0.0129 - mcc: 0.4702 - val_loss: 1.1630 - val_accuracy: 0.5699 - val_recall: 0.3255 - val_precision: 0.8025 - val_AUROC: 0.9260 - val_AUPRC: 0.6463 - val_f1_score: 0.4631 - val_balanced_accuracy: 0.6583 - val_specificity: 0.9911 - val_miss_rate: 0.6745 - val_fall_out: 0.0089 - val_mcc: 0.4814
Epoch 7/100
63/63 [==============================] - 3s 48ms/step - loss: 1.1624 - accuracy: 0.5679 - recall: 0.3756 - precision: 0.7583 - AUROC: 0.9239 - AUPRC: 0.6474 - f1_score: 0.5024 - balanced_accuracy: 0.6812 - specificity: 0.9867 - miss_rate: 0.6244 - fall_out: 0.0133 - mcc: 0.5009 - val_loss: 1.0337 - val_accuracy: 0.6370 - val_recall: 0.4347 - val_precision: 0.7799 - val_AUROC: 0.9401 - val_AUPRC: 0.7073 - val_f1_score: 0.5582 - val_balanced_accuracy: 0.7105 - val_specificity: 0.9864 - val_miss_rate: 0.5653 - val_fall_out: 0.0136 - val_mcc: 0.5506
Epoch 8/100
63/63 [==============================] - 3s 49ms/step - loss: 1.0659 - accuracy: 0.6167 - recall: 0.4450 - precision: 0.7666 - AUROC: 0.9361 - AUPRC: 0.6941 - f1_score: 0.5631 - balanced_accuracy: 0.7150 - specificity: 0.9849 - miss_rate: 0.5550 - fall_out: 0.0151 - mcc: 0.5516 - val_loss: 0.9517 - val_accuracy: 0.6510 - val_recall: 0.4907 - val_precision: 0.7834 - val_AUROC: 0.9494 - val_AUPRC: 0.7395 - val_f1_score: 0.6034 - val_balanced_accuracy: 0.7378 - val_specificity: 0.9849 - val_miss_rate: 0.5093 - val_fall_out: 0.0151 - val_mcc: 0.5889
Epoch 9/100
63/63 [==============================] - 3s 49ms/step - loss: 0.9955 - accuracy: 0.6454 - recall: 0.4937 - precision: 0.7663 - AUROC: 0.9439 - AUPRC: 0.7223 - f1_score: 0.6005 - balanced_accuracy: 0.7385 - specificity: 0.9833 - miss_rate: 0.5063 - fall_out: 0.0167 - mcc: 0.5829 - val_loss: 0.8976 - val_accuracy: 0.6785 - val_recall: 0.5689 - val_precision: 0.7911 - val_AUROC: 0.9541 - val_AUPRC: 0.7727 - val_f1_score: 0.6618 - val_balanced_accuracy: 0.7761 - val_specificity: 0.9833 - val_miss_rate: 0.4311 - val_fall_out: 0.0167 - val_mcc: 0.6412
Epoch 10/100
63/63 [==============================] - 3s 50ms/step - loss: 0.8874 - accuracy: 0.6928 - recall: 0.5734 - precision: 0.8085 - AUROC: 0.9548 - AUPRC: 0.7746 - f1_score: 0.6710 - balanced_accuracy: 0.7792 - specificity: 0.9849 - miss_rate: 0.4266 - fall_out: 0.0151 - mcc: 0.6525 - val_loss: 0.8085 - val_accuracy: 0.7276 - val_recall: 0.6234 - val_precision: 0.8294 - val_AUROC: 0.9623 - val_AUPRC: 0.8102 - val_f1_score: 0.7118 - val_balanced_accuracy: 0.8046 - val_specificity: 0.9858 - val_miss_rate: 0.3766 - val_fall_out: 0.0142 - val_mcc: 0.6932
Epoch 11/100
63/63 [==============================] - 3s 53ms/step - loss: 0.7658 - accuracy: 0.7415 - recall: 0.6443 - precision: 0.8337 - AUROC: 0.9658 - AUPRC: 0.8234 - f1_score: 0.7269 - balanced_accuracy: 0.8150 - specificity: 0.9857 - miss_rate: 0.3557 - fall_out: 0.0143 - mcc: 0.7078 - val_loss: 0.7890 - val_accuracy: 0.7216 - val_recall: 0.6139 - val_precision: 0.8461 - val_AUROC: 0.9655 - val_AUPRC: 0.8232 - val_f1_score: 0.7115 - val_balanced_accuracy: 0.8008 - val_specificity: 0.9876 - val_miss_rate: 0.3861 - val_fall_out: 0.0124 - val_mcc: 0.6956
Epoch 12/100
63/63 [==============================] - 3s 54ms/step - loss: 0.7580 - accuracy: 0.7436 - recall: 0.6449 - precision: 0.8351 - AUROC: 0.9663 - AUPRC: 0.8273 - f1_score: 0.7278 - balanced_accuracy: 0.8154 - specificity: 0.9858 - miss_rate: 0.3551 - fall_out: 0.0142 - mcc: 0.7088 - val_loss: 0.8280 - val_accuracy: 0.7111 - val_recall: 0.6249 - val_precision: 0.8067 - val_AUROC: 0.9603 - val_AUPRC: 0.7970 - val_f1_score: 0.7043 - val_balanced_accuracy: 0.8042 - val_specificity: 0.9834 - val_miss_rate: 0.3751 - val_fall_out: 0.0166 - val_mcc: 0.6826
Epoch 13/100
63/63 [==============================] - 3s 51ms/step - loss: 0.7124 - accuracy: 0.7611 - recall: 0.6700 - precision: 0.8437 - AUROC: 0.9698 - AUPRC: 0.8431 - f1_score: 0.7469 - balanced_accuracy: 0.8281 - specificity: 0.9862 - miss_rate: 0.3300 - fall_out: 0.0138 - mcc: 0.7281 - val_loss: 0.7506 - val_accuracy: 0.7541 - val_recall: 0.6625 - val_precision: 0.8352 - val_AUROC: 0.9664 - val_AUPRC: 0.8308 - val_f1_score: 0.7389 - val_balanced_accuracy: 0.8240 - val_specificity: 0.9855 - val_miss_rate: 0.3375 - val_fall_out: 0.0145 - val_mcc: 0.7193
Epoch 14/100
63/63 [==============================] - 3s 51ms/step - loss: 0.5830 - accuracy: 0.8042 - recall: 0.7342 - precision: 0.8696 - AUROC: 0.9790 - AUPRC: 0.8878 - f1_score: 0.7962 - balanced_accuracy: 0.8610 - specificity: 0.9878 - miss_rate: 0.2658 - fall_out: 0.0122 - mcc: 0.7790 - val_loss: 0.6936 - val_accuracy: 0.7732 - val_recall: 0.6895 - val_precision: 0.8448 - val_AUROC: 0.9704 - val_AUPRC: 0.8563 - val_f1_score: 0.7593 - val_balanced_accuracy: 0.8377 - val_specificity: 0.9859 - val_miss_rate: 0.3105 - val_fall_out: 0.0141 - val_mcc: 0.7401
Epoch 15/100
63/63 [==============================] - 3s 53ms/step - loss: 0.5410 - accuracy: 0.8169 - recall: 0.7541 - precision: 0.8737 - AUROC: 0.9820 - AUPRC: 0.9004 - f1_score: 0.8095 - balanced_accuracy: 0.8710 - specificity: 0.9879 - miss_rate: 0.2459 - fall_out: 0.0121 - mcc: 0.7927 - val_loss: 0.6430 - val_accuracy: 0.7802 - val_recall: 0.7321 - val_precision: 0.8417 - val_AUROC: 0.9743 - val_AUPRC: 0.8710 - val_f1_score: 0.7831 - val_balanced_accuracy: 0.8584 - val_specificity: 0.9847 - val_miss_rate: 0.2679 - val_fall_out: 0.0153 - val_mcc: 0.7631
Epoch 16/100
63/63 [==============================] - 3s 51ms/step - loss: 0.4895 - accuracy: 0.8376 - recall: 0.7821 - precision: 0.8911 - AUROC: 0.9849 - AUPRC: 0.9157 - f1_score: 0.8330 - balanced_accuracy: 0.8857 - specificity: 0.9894 - miss_rate: 0.2179 - fall_out: 0.0106 - mcc: 0.8179 - val_loss: 0.8487 - val_accuracy: 0.7311 - val_recall: 0.6880 - val_precision: 0.7860 - val_AUROC: 0.9584 - val_AUPRC: 0.8209 - val_f1_score: 0.7338 - val_balanced_accuracy: 0.8336 - val_specificity: 0.9792 - val_miss_rate: 0.3120 - val_fall_out: 0.0208 - val_mcc: 0.7083
Epoch 17/100
63/63 [==============================] - 3s 51ms/step - loss: 0.4397 - accuracy: 0.8546 - recall: 0.8059 - precision: 0.8967 - AUROC: 0.9874 - AUPRC: 0.9298 - f1_score: 0.8489 - balanced_accuracy: 0.8978 - specificity: 0.9897 - miss_rate: 0.1941 - fall_out: 0.0103 - mcc: 0.8345 - val_loss: 0.6773 - val_accuracy: 0.7772 - val_recall: 0.7326 - val_precision: 0.8294 - val_AUROC: 0.9723 - val_AUPRC: 0.8593 - val_f1_score: 0.7780 - val_balanced_accuracy: 0.8579 - val_specificity: 0.9833 - val_miss_rate: 0.2674 - val_fall_out: 0.0167 - val_mcc: 0.7568
250/250 [==============================] - 2s 7ms/step - loss: 0.3460 - accuracy: 0.8815 - recall: 0.8419 - precision: 0.9188 - AUROC: 0.9933 - AUPRC: 0.9549 - f1_score: 0.8787 - balanced_accuracy: 0.9168 - specificity: 0.9917 - miss_rate: 0.1581 - fall_out: 0.0083 - mcc: 0.8669
63/63 [==============================] - 0s 8ms/step - loss: 0.6773 - accuracy: 0.7772 - recall: 0.7326 - precision: 0.8294 - AUROC: 0.9723 - AUPRC: 0.8593 - f1_score: 0.7780 - balanced_accuracy: 0.8579 - specificity: 0.9833 - miss_rate: 0.2674 - fall_out: 0.0167 - mcc: 0.7568
7it [06:01, 54.45s/it]
-- HOLDOUT 8
Model: "CNN_Mfccs_3s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_332 (Conv2D) (None, 20, 130, 256) 2560
max_pooling2d_332 (MaxPooli (None, 10, 65, 256) 0
ng2D)
conv2d_333 (Conv2D) (None, 10, 65, 256) 590080
max_pooling2d_333 (MaxPooli (None, 5, 33, 256) 0
ng2D)
conv2d_334 (Conv2D) (None, 5, 33, 256) 590080
max_pooling2d_334 (MaxPooli (None, 3, 17, 256) 0
ng2D)
conv2d_335 (Conv2D) (None, 3, 17, 512) 1180160
max_pooling2d_335 (MaxPooli (None, 2, 9, 512) 0
ng2D)
flatten_83 (Flatten) (None, 9216) 0
dense_249 (Dense) (None, 256) 2359552
dropout_364 (Dropout) (None, 256) 0
dense_250 (Dense) (None, 256) 65792
dropout_365 (Dropout) (None, 256) 0
dense_251 (Dense) (None, 10) 2570
=================================================================
Total params: 4,790,794
Trainable params: 4,790,794
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 5s 64ms/step - loss: 2.0528 - accuracy: 0.2384 - recall: 0.0631 - precision: 0.5979 - AUROC: 0.7040 - AUPRC: 0.2507 - f1_score: 0.1142 - balanced_accuracy: 0.5292 - specificity: 0.9953 - miss_rate: 0.9369 - fall_out: 0.0047 - mcc: 0.1714 - val_loss: 1.7127 - val_accuracy: 0.3550 - val_recall: 0.2374 - val_precision: 0.5759 - val_AUROC: 0.8217 - val_AUPRC: 0.4027 - val_f1_score: 0.3362 - val_balanced_accuracy: 0.6090 - val_specificity: 0.9806 - val_miss_rate: 0.7626 - val_fall_out: 0.0194 - val_mcc: 0.3289
Epoch 2/100
63/63 [==============================] - 3s 50ms/step - loss: 1.6802 - accuracy: 0.3702 - recall: 0.1672 - precision: 0.6892 - AUROC: 0.8271 - AUPRC: 0.4095 - f1_score: 0.2691 - balanced_accuracy: 0.5794 - specificity: 0.9916 - miss_rate: 0.8328 - fall_out: 0.0084 - mcc: 0.3097 - val_loss: 1.5339 - val_accuracy: 0.4296 - val_recall: 0.2318 - val_precision: 0.6157 - val_AUROC: 0.8663 - val_AUPRC: 0.4779 - val_f1_score: 0.3368 - val_balanced_accuracy: 0.6079 - val_specificity: 0.9839 - val_miss_rate: 0.7682 - val_fall_out: 0.0161 - val_mcc: 0.3400
Epoch 3/100
63/63 [==============================] - 3s 50ms/step - loss: 1.5035 - accuracy: 0.4378 - recall: 0.2194 - precision: 0.6950 - AUROC: 0.8690 - AUPRC: 0.4825 - f1_score: 0.3336 - balanced_accuracy: 0.6044 - specificity: 0.9893 - miss_rate: 0.7806 - fall_out: 0.0107 - mcc: 0.3581 - val_loss: 1.3821 - val_accuracy: 0.4762 - val_recall: 0.2499 - val_precision: 0.7170 - val_AUROC: 0.8926 - val_AUPRC: 0.5373 - val_f1_score: 0.3706 - val_balanced_accuracy: 0.6195 - val_specificity: 0.9890 - val_miss_rate: 0.7501 - val_fall_out: 0.0110 - val_mcc: 0.3908
Epoch 4/100
63/63 [==============================] - 3s 50ms/step - loss: 1.3555 - accuracy: 0.4892 - recall: 0.2818 - precision: 0.7291 - AUROC: 0.8953 - AUPRC: 0.5547 - f1_score: 0.4065 - balanced_accuracy: 0.6351 - specificity: 0.9884 - miss_rate: 0.7182 - fall_out: 0.0116 - mcc: 0.4205 - val_loss: 1.2231 - val_accuracy: 0.5268 - val_recall: 0.3365 - val_precision: 0.7585 - val_AUROC: 0.9155 - val_AUPRC: 0.6119 - val_f1_score: 0.4662 - val_balanced_accuracy: 0.6623 - val_specificity: 0.9881 - val_miss_rate: 0.6635 - val_fall_out: 0.0119 - val_mcc: 0.4729
Epoch 5/100
63/63 [==============================] - 4s 56ms/step - loss: 1.2411 - accuracy: 0.5412 - recall: 0.3433 - precision: 0.7317 - AUROC: 0.9131 - AUPRC: 0.6082 - f1_score: 0.4673 - balanced_accuracy: 0.6647 - specificity: 0.9860 - miss_rate: 0.6567 - fall_out: 0.0140 - mcc: 0.4672 - val_loss: 1.1027 - val_accuracy: 0.6024 - val_recall: 0.3761 - val_precision: 0.8075 - val_AUROC: 0.9328 - val_AUPRC: 0.6754 - val_f1_score: 0.5132 - val_balanced_accuracy: 0.6831 - val_specificity: 0.9900 - val_miss_rate: 0.6239 - val_fall_out: 0.0100 - val_mcc: 0.5212
Epoch 6/100
63/63 [==============================] - 4s 58ms/step - loss: 1.2088 - accuracy: 0.5631 - recall: 0.3686 - precision: 0.7454 - AUROC: 0.9178 - AUPRC: 0.6273 - f1_score: 0.4933 - balanced_accuracy: 0.6773 - specificity: 0.9860 - miss_rate: 0.6314 - fall_out: 0.0140 - mcc: 0.4907 - val_loss: 1.0667 - val_accuracy: 0.6009 - val_recall: 0.3911 - val_precision: 0.7748 - val_AUROC: 0.9375 - val_AUPRC: 0.6829 - val_f1_score: 0.5198 - val_balanced_accuracy: 0.6892 - val_specificity: 0.9874 - val_miss_rate: 0.6089 - val_fall_out: 0.0126 - val_mcc: 0.5186
Epoch 7/100
63/63 [==============================] - 3s 55ms/step - loss: 1.1022 - accuracy: 0.6073 - recall: 0.4330 - precision: 0.7589 - AUROC: 0.9312 - AUPRC: 0.6764 - f1_score: 0.5514 - balanced_accuracy: 0.7089 - specificity: 0.9847 - miss_rate: 0.5670 - fall_out: 0.0153 - mcc: 0.5403 - val_loss: 0.9646 - val_accuracy: 0.6485 - val_recall: 0.5143 - val_precision: 0.7664 - val_AUROC: 0.9471 - val_AUPRC: 0.7317 - val_f1_score: 0.6155 - val_balanced_accuracy: 0.7484 - val_specificity: 0.9826 - val_miss_rate: 0.4857 - val_fall_out: 0.0174 - val_mcc: 0.5958
Epoch 8/100
63/63 [==============================] - 3s 53ms/step - loss: 0.9809 - accuracy: 0.6561 - recall: 0.5078 - precision: 0.7802 - AUROC: 0.9455 - AUPRC: 0.7305 - f1_score: 0.6152 - balanced_accuracy: 0.7459 - specificity: 0.9841 - miss_rate: 0.4922 - fall_out: 0.0159 - mcc: 0.5982 - val_loss: 0.8766 - val_accuracy: 0.6920 - val_recall: 0.5774 - val_precision: 0.7990 - val_AUROC: 0.9565 - val_AUPRC: 0.7744 - val_f1_score: 0.6703 - val_balanced_accuracy: 0.7806 - val_specificity: 0.9839 - val_miss_rate: 0.4226 - val_fall_out: 0.0161 - val_mcc: 0.6503
Epoch 9/100
63/63 [==============================] - 3s 51ms/step - loss: 0.9208 - accuracy: 0.6787 - recall: 0.5515 - precision: 0.7915 - AUROC: 0.9515 - AUPRC: 0.7585 - f1_score: 0.6500 - balanced_accuracy: 0.7677 - specificity: 0.9839 - miss_rate: 0.4485 - fall_out: 0.0161 - mcc: 0.6308 - val_loss: 0.8925 - val_accuracy: 0.6845 - val_recall: 0.5568 - val_precision: 0.8189 - val_AUROC: 0.9549 - val_AUPRC: 0.7746 - val_f1_score: 0.6629 - val_balanced_accuracy: 0.7716 - val_specificity: 0.9863 - val_miss_rate: 0.4432 - val_fall_out: 0.0137 - val_mcc: 0.6472
Epoch 10/100
63/63 [==============================] - 3s 50ms/step - loss: 0.8676 - accuracy: 0.6961 - recall: 0.5730 - precision: 0.8079 - AUROC: 0.9570 - AUPRC: 0.7812 - f1_score: 0.6705 - balanced_accuracy: 0.7789 - specificity: 0.9849 - miss_rate: 0.4270 - fall_out: 0.0151 - mcc: 0.6520 - val_loss: 0.8332 - val_accuracy: 0.7046 - val_recall: 0.5969 - val_precision: 0.8054 - val_AUROC: 0.9608 - val_AUPRC: 0.7926 - val_f1_score: 0.6856 - val_balanced_accuracy: 0.7904 - val_specificity: 0.9840 - val_miss_rate: 0.4031 - val_fall_out: 0.0160 - val_mcc: 0.6652
Epoch 11/100
63/63 [==============================] - 3s 49ms/step - loss: 0.7795 - accuracy: 0.7316 - recall: 0.6318 - precision: 0.8270 - AUROC: 0.9647 - AUPRC: 0.8197 - f1_score: 0.7163 - balanced_accuracy: 0.8085 - specificity: 0.9853 - miss_rate: 0.3682 - fall_out: 0.0147 - mcc: 0.6969 - val_loss: 0.7809 - val_accuracy: 0.7331 - val_recall: 0.6149 - val_precision: 0.8510 - val_AUROC: 0.9659 - val_AUPRC: 0.8218 - val_f1_score: 0.7140 - val_balanced_accuracy: 0.8015 - val_specificity: 0.9880 - val_miss_rate: 0.3851 - val_fall_out: 0.0120 - val_mcc: 0.6986
Epoch 12/100
63/63 [==============================] - 3s 52ms/step - loss: 0.7493 - accuracy: 0.7381 - recall: 0.6433 - precision: 0.8309 - AUROC: 0.9675 - AUPRC: 0.8276 - f1_score: 0.7252 - balanced_accuracy: 0.8144 - specificity: 0.9855 - miss_rate: 0.3567 - fall_out: 0.0145 - mcc: 0.7058 - val_loss: 0.8611 - val_accuracy: 0.7016 - val_recall: 0.5769 - val_precision: 0.8000 - val_AUROC: 0.9578 - val_AUPRC: 0.7848 - val_f1_score: 0.6704 - val_balanced_accuracy: 0.7804 - val_specificity: 0.9840 - val_miss_rate: 0.4231 - val_fall_out: 0.0160 - val_mcc: 0.6505
Epoch 13/100
63/63 [==============================] - 3s 53ms/step - loss: 0.6798 - accuracy: 0.7687 - recall: 0.6802 - precision: 0.8485 - AUROC: 0.9729 - AUPRC: 0.8538 - f1_score: 0.7551 - balanced_accuracy: 0.8334 - specificity: 0.9865 - miss_rate: 0.3198 - fall_out: 0.0135 - mcc: 0.7366 - val_loss: 0.7767 - val_accuracy: 0.7406 - val_recall: 0.6800 - val_precision: 0.8117 - val_AUROC: 0.9653 - val_AUPRC: 0.8270 - val_f1_score: 0.7401 - val_balanced_accuracy: 0.8312 - val_specificity: 0.9825 - val_miss_rate: 0.3200 - val_fall_out: 0.0175 - val_mcc: 0.7174
Epoch 14/100
63/63 [==============================] - 3s 51ms/step - loss: 0.6263 - accuracy: 0.7905 - recall: 0.7113 - precision: 0.8633 - AUROC: 0.9765 - AUPRC: 0.8764 - f1_score: 0.7800 - balanced_accuracy: 0.8494 - specificity: 0.9875 - miss_rate: 0.2887 - fall_out: 0.0125 - mcc: 0.7624 - val_loss: 0.6818 - val_accuracy: 0.7762 - val_recall: 0.7206 - val_precision: 0.8376 - val_AUROC: 0.9720 - val_AUPRC: 0.8597 - val_f1_score: 0.7747 - val_balanced_accuracy: 0.8525 - val_specificity: 0.9845 - val_miss_rate: 0.2794 - val_fall_out: 0.0155 - val_mcc: 0.7543
Epoch 15/100
63/63 [==============================] - 3s 52ms/step - loss: 0.5394 - accuracy: 0.8210 - recall: 0.7611 - precision: 0.8760 - AUROC: 0.9822 - AUPRC: 0.9023 - f1_score: 0.8146 - balanced_accuracy: 0.8746 - specificity: 0.9880 - miss_rate: 0.2389 - fall_out: 0.0120 - mcc: 0.7979 - val_loss: 0.6576 - val_accuracy: 0.7762 - val_recall: 0.7161 - val_precision: 0.8348 - val_AUROC: 0.9736 - val_AUPRC: 0.8667 - val_f1_score: 0.7709 - val_balanced_accuracy: 0.8502 - val_specificity: 0.9843 - val_miss_rate: 0.2839 - val_fall_out: 0.0157 - val_mcc: 0.7503
Epoch 16/100
63/63 [==============================] - 3s 49ms/step - loss: 0.5203 - accuracy: 0.8253 - recall: 0.7668 - precision: 0.8780 - AUROC: 0.9829 - AUPRC: 0.9052 - f1_score: 0.8186 - balanced_accuracy: 0.8775 - specificity: 0.9882 - miss_rate: 0.2332 - fall_out: 0.0118 - mcc: 0.8022 - val_loss: 0.6731 - val_accuracy: 0.7842 - val_recall: 0.7271 - val_precision: 0.8321 - val_AUROC: 0.9721 - val_AUPRC: 0.8640 - val_f1_score: 0.7761 - val_balanced_accuracy: 0.8554 - val_specificity: 0.9837 - val_miss_rate: 0.2729 - val_fall_out: 0.0163 - val_mcc: 0.7551
Epoch 17/100
63/63 [==============================] - 3s 50ms/step - loss: 0.4608 - accuracy: 0.8448 - recall: 0.7940 - precision: 0.8886 - AUROC: 0.9868 - AUPRC: 0.9235 - f1_score: 0.8386 - balanced_accuracy: 0.8914 - specificity: 0.9889 - miss_rate: 0.2060 - fall_out: 0.0111 - mcc: 0.8234 - val_loss: 0.7349 - val_accuracy: 0.7626 - val_recall: 0.7156 - val_precision: 0.8101 - val_AUROC: 0.9670 - val_AUPRC: 0.8438 - val_f1_score: 0.7599 - val_balanced_accuracy: 0.8485 - val_specificity: 0.9814 - val_miss_rate: 0.2844 - val_fall_out: 0.0186 - val_mcc: 0.7368
250/250 [==============================] - 2s 7ms/step - loss: 0.3957 - accuracy: 0.8560 - recall: 0.8201 - precision: 0.8960 - AUROC: 0.9904 - AUPRC: 0.9393 - f1_score: 0.8564 - balanced_accuracy: 0.9048 - specificity: 0.9894 - miss_rate: 0.1799 - fall_out: 0.0106 - mcc: 0.8422
63/63 [==============================] - 0s 7ms/step - loss: 0.7349 - accuracy: 0.7626 - recall: 0.7156 - precision: 0.8101 - AUROC: 0.9670 - AUPRC: 0.8438 - f1_score: 0.7599 - balanced_accuracy: 0.8485 - specificity: 0.9814 - miss_rate: 0.2844 - fall_out: 0.0186 - mcc: 0.7368
8it [07:01, 56.20s/it]
-- HOLDOUT 9
Model: "CNN_Mfccs_3s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_336 (Conv2D) (None, 20, 130, 256) 2560
max_pooling2d_336 (MaxPooli (None, 10, 65, 256) 0
ng2D)
conv2d_337 (Conv2D) (None, 10, 65, 256) 590080
max_pooling2d_337 (MaxPooli (None, 5, 33, 256) 0
ng2D)
conv2d_338 (Conv2D) (None, 5, 33, 256) 590080
max_pooling2d_338 (MaxPooli (None, 3, 17, 256) 0
ng2D)
conv2d_339 (Conv2D) (None, 3, 17, 512) 1180160
max_pooling2d_339 (MaxPooli (None, 2, 9, 512) 0
ng2D)
flatten_84 (Flatten) (None, 9216) 0
dense_252 (Dense) (None, 256) 2359552
dropout_366 (Dropout) (None, 256) 0
dense_253 (Dense) (None, 256) 65792
dropout_367 (Dropout) (None, 256) 0
dense_254 (Dense) (None, 10) 2570
=================================================================
Total params: 4,790,794
Trainable params: 4,790,794
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 4s 56ms/step - loss: 2.1236 - accuracy: 0.1933 - recall: 0.0426 - precision: 0.6028 - AUROC: 0.6703 - AUPRC: 0.2127 - f1_score: 0.0796 - balanced_accuracy: 0.5197 - specificity: 0.9969 - miss_rate: 0.9574 - fall_out: 0.0031 - mcc: 0.1414 - val_loss: 1.7240 - val_accuracy: 0.3721 - val_recall: 0.1432 - val_precision: 0.8462 - val_AUROC: 0.8265 - val_AUPRC: 0.4250 - val_f1_score: 0.2450 - val_balanced_accuracy: 0.5702 - val_specificity: 0.9971 - val_miss_rate: 0.8568 - val_fall_out: 0.0029 - val_mcc: 0.3263
Epoch 2/100
63/63 [==============================] - 3s 48ms/step - loss: 1.7190 - accuracy: 0.3689 - recall: 0.1516 - precision: 0.6798 - AUROC: 0.8185 - AUPRC: 0.3933 - f1_score: 0.2478 - balanced_accuracy: 0.5718 - specificity: 0.9921 - miss_rate: 0.8484 - fall_out: 0.0079 - mcc: 0.2918 - val_loss: 1.5431 - val_accuracy: 0.4171 - val_recall: 0.1803 - val_precision: 0.8018 - val_AUROC: 0.8576 - val_AUPRC: 0.4821 - val_f1_score: 0.2944 - val_balanced_accuracy: 0.5877 - val_specificity: 0.9950 - val_miss_rate: 0.8197 - val_fall_out: 0.0050 - val_mcc: 0.3548
Epoch 3/100
63/63 [==============================] - 3s 48ms/step - loss: 1.5338 - accuracy: 0.4252 - recall: 0.2152 - precision: 0.7182 - AUROC: 0.8615 - AUPRC: 0.4807 - f1_score: 0.3311 - balanced_accuracy: 0.6029 - specificity: 0.9906 - miss_rate: 0.7848 - fall_out: 0.0094 - mcc: 0.3622 - val_loss: 1.4957 - val_accuracy: 0.4226 - val_recall: 0.2429 - val_precision: 0.7282 - val_AUROC: 0.8683 - val_AUPRC: 0.4999 - val_f1_score: 0.3643 - val_balanced_accuracy: 0.6164 - val_specificity: 0.9899 - val_miss_rate: 0.7571 - val_fall_out: 0.0101 - val_mcc: 0.3890
Epoch 4/100
63/63 [==============================] - 3s 48ms/step - loss: 1.4266 - accuracy: 0.4671 - recall: 0.2578 - precision: 0.7262 - AUROC: 0.8823 - AUPRC: 0.5274 - f1_score: 0.3805 - balanced_accuracy: 0.6235 - specificity: 0.9892 - miss_rate: 0.7422 - fall_out: 0.0108 - mcc: 0.4004 - val_loss: 1.3742 - val_accuracy: 0.4942 - val_recall: 0.3085 - val_precision: 0.7360 - val_AUROC: 0.8906 - val_AUPRC: 0.5677 - val_f1_score: 0.4347 - val_balanced_accuracy: 0.6481 - val_specificity: 0.9877 - val_miss_rate: 0.6915 - val_fall_out: 0.0123 - val_mcc: 0.4434
Epoch 5/100
63/63 [==============================] - 3s 48ms/step - loss: 1.3185 - accuracy: 0.5076 - recall: 0.3031 - precision: 0.7272 - AUROC: 0.9012 - AUPRC: 0.5736 - f1_score: 0.4279 - balanced_accuracy: 0.6452 - specificity: 0.9874 - miss_rate: 0.6969 - fall_out: 0.0126 - mcc: 0.4360 - val_loss: 1.1809 - val_accuracy: 0.5513 - val_recall: 0.3530 - val_precision: 0.7622 - val_AUROC: 0.9212 - val_AUPRC: 0.6371 - val_f1_score: 0.4825 - val_balanced_accuracy: 0.6704 - val_specificity: 0.9878 - val_miss_rate: 0.6470 - val_fall_out: 0.0122 - val_mcc: 0.4864
Epoch 6/100
63/63 [==============================] - 3s 48ms/step - loss: 1.2140 - accuracy: 0.5561 - recall: 0.3612 - precision: 0.7518 - AUROC: 0.9168 - AUPRC: 0.6256 - f1_score: 0.4880 - balanced_accuracy: 0.6740 - specificity: 0.9868 - miss_rate: 0.6388 - fall_out: 0.0132 - mcc: 0.4881 - val_loss: 1.1165 - val_accuracy: 0.5864 - val_recall: 0.3996 - val_precision: 0.7862 - val_AUROC: 0.9310 - val_AUPRC: 0.6708 - val_f1_score: 0.5299 - val_balanced_accuracy: 0.6938 - val_specificity: 0.9879 - val_miss_rate: 0.6004 - val_fall_out: 0.0121 - val_mcc: 0.5293
Epoch 7/100
63/63 [==============================] - 3s 48ms/step - loss: 1.1535 - accuracy: 0.5892 - recall: 0.4038 - precision: 0.7550 - AUROC: 0.9250 - AUPRC: 0.6576 - f1_score: 0.5262 - balanced_accuracy: 0.6946 - specificity: 0.9854 - miss_rate: 0.5962 - fall_out: 0.0146 - mcc: 0.5190 - val_loss: 1.0346 - val_accuracy: 0.6299 - val_recall: 0.4577 - val_precision: 0.7792 - val_AUROC: 0.9401 - val_AUPRC: 0.7050 - val_f1_score: 0.5767 - val_balanced_accuracy: 0.7216 - val_specificity: 0.9856 - val_miss_rate: 0.5423 - val_fall_out: 0.0144 - val_mcc: 0.5656
Epoch 8/100
63/63 [==============================] - 3s 48ms/step - loss: 1.0439 - accuracy: 0.6326 - recall: 0.4743 - precision: 0.7725 - AUROC: 0.9383 - AUPRC: 0.7084 - f1_score: 0.5878 - balanced_accuracy: 0.7294 - specificity: 0.9845 - miss_rate: 0.5257 - fall_out: 0.0155 - mcc: 0.5734 - val_loss: 1.0040 - val_accuracy: 0.6355 - val_recall: 0.4697 - val_precision: 0.7843 - val_AUROC: 0.9446 - val_AUPRC: 0.7202 - val_f1_score: 0.5875 - val_balanced_accuracy: 0.7277 - val_specificity: 0.9856 - val_miss_rate: 0.5303 - val_fall_out: 0.0144 - val_mcc: 0.5757
Epoch 9/100
63/63 [==============================] - 3s 48ms/step - loss: 0.9780 - accuracy: 0.6643 - recall: 0.5219 - precision: 0.7880 - AUROC: 0.9456 - AUPRC: 0.7383 - f1_score: 0.6279 - balanced_accuracy: 0.7532 - specificity: 0.9844 - miss_rate: 0.4781 - fall_out: 0.0156 - mcc: 0.6108 - val_loss: 0.8966 - val_accuracy: 0.6855 - val_recall: 0.5603 - val_precision: 0.8022 - val_AUROC: 0.9548 - val_AUPRC: 0.7668 - val_f1_score: 0.6598 - val_balanced_accuracy: 0.7725 - val_specificity: 0.9846 - val_miss_rate: 0.4397 - val_fall_out: 0.0154 - val_mcc: 0.6414
Epoch 10/100
63/63 [==============================] - 3s 48ms/step - loss: 0.9306 - accuracy: 0.6824 - recall: 0.5540 - precision: 0.8030 - AUROC: 0.9502 - AUPRC: 0.7610 - f1_score: 0.6556 - balanced_accuracy: 0.7694 - specificity: 0.9849 - miss_rate: 0.4460 - fall_out: 0.0151 - mcc: 0.6379 - val_loss: 1.0241 - val_accuracy: 0.6440 - val_recall: 0.5188 - val_precision: 0.7801 - val_AUROC: 0.9400 - val_AUPRC: 0.7275 - val_f1_score: 0.6232 - val_balanced_accuracy: 0.7513 - val_specificity: 0.9838 - val_miss_rate: 0.4812 - val_fall_out: 0.0162 - val_mcc: 0.6051
Epoch 11/100
63/63 [==============================] - 3s 48ms/step - loss: 0.8637 - accuracy: 0.7050 - recall: 0.5877 - precision: 0.8125 - AUROC: 0.9573 - AUPRC: 0.7869 - f1_score: 0.6820 - balanced_accuracy: 0.7863 - specificity: 0.9849 - miss_rate: 0.4123 - fall_out: 0.0151 - mcc: 0.6632 - val_loss: 0.7985 - val_accuracy: 0.7226 - val_recall: 0.6254 - val_precision: 0.8217 - val_AUROC: 0.9640 - val_AUPRC: 0.8102 - val_f1_score: 0.7103 - val_balanced_accuracy: 0.8052 - val_specificity: 0.9849 - val_miss_rate: 0.3746 - val_fall_out: 0.0151 - val_mcc: 0.6905
Epoch 12/100
63/63 [==============================] - 3s 48ms/step - loss: 0.7823 - accuracy: 0.7356 - recall: 0.6414 - precision: 0.8297 - AUROC: 0.9644 - AUPRC: 0.8205 - f1_score: 0.7235 - balanced_accuracy: 0.8134 - specificity: 0.9854 - miss_rate: 0.3586 - fall_out: 0.0146 - mcc: 0.7041 - val_loss: 0.8332 - val_accuracy: 0.7076 - val_recall: 0.6209 - val_precision: 0.7979 - val_AUROC: 0.9603 - val_AUPRC: 0.7920 - val_f1_score: 0.6984 - val_balanced_accuracy: 0.8017 - val_specificity: 0.9825 - val_miss_rate: 0.3791 - val_fall_out: 0.0175 - val_mcc: 0.6758
Epoch 13/100
63/63 [==============================] - 3s 48ms/step - loss: 0.6864 - accuracy: 0.7654 - recall: 0.6827 - precision: 0.8497 - AUROC: 0.9722 - AUPRC: 0.8547 - f1_score: 0.7571 - balanced_accuracy: 0.8347 - specificity: 0.9866 - miss_rate: 0.3173 - fall_out: 0.0134 - mcc: 0.7387 - val_loss: 0.7631 - val_accuracy: 0.7466 - val_recall: 0.6785 - val_precision: 0.8187 - val_AUROC: 0.9647 - val_AUPRC: 0.8351 - val_f1_score: 0.7421 - val_balanced_accuracy: 0.8309 - val_specificity: 0.9833 - val_miss_rate: 0.3215 - val_fall_out: 0.0167 - val_mcc: 0.7202
Epoch 14/100
63/63 [==============================] - 3s 48ms/step - loss: 0.6388 - accuracy: 0.7863 - recall: 0.7077 - precision: 0.8625 - AUROC: 0.9753 - AUPRC: 0.8704 - f1_score: 0.7774 - balanced_accuracy: 0.8476 - specificity: 0.9875 - miss_rate: 0.2923 - fall_out: 0.0125 - mcc: 0.7599 - val_loss: 0.7474 - val_accuracy: 0.7541 - val_recall: 0.6965 - val_precision: 0.8078 - val_AUROC: 0.9666 - val_AUPRC: 0.8340 - val_f1_score: 0.7481 - val_balanced_accuracy: 0.8391 - val_specificity: 0.9816 - val_miss_rate: 0.3035 - val_fall_out: 0.0184 - val_mcc: 0.7247
Epoch 15/100
63/63 [==============================] - 3s 48ms/step - loss: 0.5569 - accuracy: 0.8145 - recall: 0.7541 - precision: 0.8758 - AUROC: 0.9809 - AUPRC: 0.8971 - f1_score: 0.8104 - balanced_accuracy: 0.8711 - specificity: 0.9881 - miss_rate: 0.2459 - fall_out: 0.0119 - mcc: 0.7938 - val_loss: 0.6579 - val_accuracy: 0.7742 - val_recall: 0.7306 - val_precision: 0.8347 - val_AUROC: 0.9734 - val_AUPRC: 0.8656 - val_f1_score: 0.7792 - val_balanced_accuracy: 0.8573 - val_specificity: 0.9839 - val_miss_rate: 0.2694 - val_fall_out: 0.0161 - val_mcc: 0.7585
Epoch 16/100
63/63 [==============================] - 3s 47ms/step - loss: 0.5080 - accuracy: 0.8304 - recall: 0.7794 - precision: 0.8851 - AUROC: 0.9834 - AUPRC: 0.9115 - f1_score: 0.8289 - balanced_accuracy: 0.8841 - specificity: 0.9888 - miss_rate: 0.2206 - fall_out: 0.0112 - mcc: 0.8132 - val_loss: 0.6393 - val_accuracy: 0.7827 - val_recall: 0.7301 - val_precision: 0.8482 - val_AUROC: 0.9758 - val_AUPRC: 0.8728 - val_f1_score: 0.7847 - val_balanced_accuracy: 0.8578 - val_specificity: 0.9855 - val_miss_rate: 0.2699 - val_fall_out: 0.0145 - val_mcc: 0.7654
Epoch 17/100
63/63 [==============================] - 3s 48ms/step - loss: 0.4806 - accuracy: 0.8421 - recall: 0.7833 - precision: 0.8925 - AUROC: 0.9850 - AUPRC: 0.9196 - f1_score: 0.8344 - balanced_accuracy: 0.8864 - specificity: 0.9895 - miss_rate: 0.2167 - fall_out: 0.0105 - mcc: 0.8194 - val_loss: 0.6534 - val_accuracy: 0.7787 - val_recall: 0.7196 - val_precision: 0.8423 - val_AUROC: 0.9740 - val_AUPRC: 0.8668 - val_f1_score: 0.7761 - val_balanced_accuracy: 0.8523 - val_specificity: 0.9850 - val_miss_rate: 0.2804 - val_fall_out: 0.0150 - val_mcc: 0.7562
Epoch 18/100
63/63 [==============================] - 3s 48ms/step - loss: 0.4476 - accuracy: 0.8463 - recall: 0.8025 - precision: 0.8947 - AUROC: 0.9869 - AUPRC: 0.9281 - f1_score: 0.8461 - balanced_accuracy: 0.8960 - specificity: 0.9895 - miss_rate: 0.1975 - fall_out: 0.0105 - mcc: 0.8315 - val_loss: 0.6985 - val_accuracy: 0.7792 - val_recall: 0.7361 - val_precision: 0.8235 - val_AUROC: 0.9685 - val_AUPRC: 0.8608 - val_f1_score: 0.7774 - val_balanced_accuracy: 0.8593 - val_specificity: 0.9825 - val_miss_rate: 0.2639 - val_fall_out: 0.0175 - val_mcc: 0.7556
250/250 [==============================] - 2s 7ms/step - loss: 0.2933 - accuracy: 0.9042 - recall: 0.8651 - precision: 0.9345 - AUROC: 0.9949 - AUPRC: 0.9671 - f1_score: 0.8985 - balanced_accuracy: 0.9292 - specificity: 0.9933 - miss_rate: 0.1349 - fall_out: 0.0067 - mcc: 0.8885
63/63 [==============================] - 0s 7ms/step - loss: 0.6985 - accuracy: 0.7792 - recall: 0.7361 - precision: 0.8235 - AUROC: 0.9685 - AUPRC: 0.8608 - f1_score: 0.7774 - balanced_accuracy: 0.8593 - specificity: 0.9825 - miss_rate: 0.2639 - fall_out: 0.0175 - mcc: 0.7556
9it [07:59, 56.75s/it]
-- HOLDOUT 10
Model: "CNN_Mfccs_3s"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_340 (Conv2D) (None, 20, 130, 256) 2560
max_pooling2d_340 (MaxPooli (None, 10, 65, 256) 0
ng2D)
conv2d_341 (Conv2D) (None, 10, 65, 256) 590080
max_pooling2d_341 (MaxPooli (None, 5, 33, 256) 0
ng2D)
conv2d_342 (Conv2D) (None, 5, 33, 256) 590080
max_pooling2d_342 (MaxPooli (None, 3, 17, 256) 0
ng2D)
conv2d_343 (Conv2D) (None, 3, 17, 512) 1180160
max_pooling2d_343 (MaxPooli (None, 2, 9, 512) 0
ng2D)
flatten_85 (Flatten) (None, 9216) 0
dense_255 (Dense) (None, 256) 2359552
dropout_368 (Dropout) (None, 256) 0
dense_256 (Dense) (None, 256) 65792
dropout_369 (Dropout) (None, 256) 0
dense_257 (Dense) (None, 10) 2570
=================================================================
Total params: 4,790,794
Trainable params: 4,790,794
Non-trainable params: 0
_________________________________________________________________
- Training model:
Epoch 1/100
63/63 [==============================] - 4s 55ms/step - loss: 2.0703 - accuracy: 0.2267 - recall: 0.0456 - precision: 0.5260 - AUROC: 0.7016 - AUPRC: 0.2348 - f1_score: 0.0839 - balanced_accuracy: 0.5205 - specificity: 0.9954 - miss_rate: 0.9544 - fall_out: 0.0046 - mcc: 0.1328 - val_loss: 1.7114 - val_accuracy: 0.3806 - val_recall: 0.2023 - val_precision: 0.6745 - val_AUROC: 0.8162 - val_AUPRC: 0.4078 - val_f1_score: 0.3112 - val_balanced_accuracy: 0.5957 - val_specificity: 0.9892 - val_miss_rate: 0.7977 - val_fall_out: 0.0108 - val_mcc: 0.3367
Epoch 2/100
63/63 [==============================] - 3s 48ms/step - loss: 1.7043 - accuracy: 0.3568 - recall: 0.1533 - precision: 0.6689 - AUROC: 0.8217 - AUPRC: 0.3931 - f1_score: 0.2494 - balanced_accuracy: 0.5724 - specificity: 0.9916 - miss_rate: 0.8467 - fall_out: 0.0084 - mcc: 0.2904 - val_loss: 1.4916 - val_accuracy: 0.4231 - val_recall: 0.1783 - val_precision: 0.8599 - val_AUROC: 0.8718 - val_AUPRC: 0.5069 - val_f1_score: 0.2953 - val_balanced_accuracy: 0.5875 - val_specificity: 0.9968 - val_miss_rate: 0.8217 - val_fall_out: 0.0032 - val_mcc: 0.3686
Epoch 3/100
63/63 [==============================] - 3s 48ms/step - loss: 1.5030 - accuracy: 0.4418 - recall: 0.2196 - precision: 0.7094 - AUROC: 0.8680 - AUPRC: 0.4841 - f1_score: 0.3353 - balanced_accuracy: 0.6048 - specificity: 0.9900 - miss_rate: 0.7804 - fall_out: 0.0100 - mcc: 0.3630 - val_loss: 1.3097 - val_accuracy: 0.5058 - val_recall: 0.2514 - val_precision: 0.8084 - val_AUROC: 0.9034 - val_AUPRC: 0.5803 - val_f1_score: 0.3835 - val_balanced_accuracy: 0.6224 - val_specificity: 0.9934 - val_miss_rate: 0.7486 - val_fall_out: 0.0066 - val_mcc: 0.4230
Epoch 4/100
63/63 [==============================] - 3s 48ms/step - loss: 1.3774 - accuracy: 0.4801 - recall: 0.2807 - precision: 0.7262 - AUROC: 0.8914 - AUPRC: 0.5444 - f1_score: 0.4049 - balanced_accuracy: 0.6345 - specificity: 0.9882 - miss_rate: 0.7193 - fall_out: 0.0118 - mcc: 0.4185 - val_loss: 1.3561 - val_accuracy: 0.4927 - val_recall: 0.2979 - val_precision: 0.7126 - val_AUROC: 0.8942 - val_AUPRC: 0.5626 - val_f1_score: 0.4202 - val_balanced_accuracy: 0.6423 - val_specificity: 0.9866 - val_miss_rate: 0.7021 - val_fall_out: 0.0134 - val_mcc: 0.4265
Epoch 5/100
63/63 [==============================] - 3s 48ms/step - loss: 1.3118 - accuracy: 0.5121 - recall: 0.3112 - precision: 0.7335 - AUROC: 0.9021 - AUPRC: 0.5756 - f1_score: 0.4370 - balanced_accuracy: 0.6493 - specificity: 0.9874 - miss_rate: 0.6888 - fall_out: 0.0126 - mcc: 0.4445 - val_loss: 1.1759 - val_accuracy: 0.5648 - val_recall: 0.3195 - val_precision: 0.7955 - val_AUROC: 0.9230 - val_AUPRC: 0.6391 - val_f1_score: 0.4559 - val_balanced_accuracy: 0.6552 - val_specificity: 0.9909 - val_miss_rate: 0.6805 - val_fall_out: 0.0091 - val_mcc: 0.4742
Epoch 6/100
63/63 [==============================] - 3s 48ms/step - loss: 1.1959 - accuracy: 0.5517 - recall: 0.3714 - precision: 0.7350 - AUROC: 0.9193 - AUPRC: 0.6280 - f1_score: 0.4934 - balanced_accuracy: 0.6782 - specificity: 0.9851 - miss_rate: 0.6286 - fall_out: 0.0149 - mcc: 0.4883 - val_loss: 1.0555 - val_accuracy: 0.6124 - val_recall: 0.4001 - val_precision: 0.8103 - val_AUROC: 0.9388 - val_AUPRC: 0.7016 - val_f1_score: 0.5357 - val_balanced_accuracy: 0.6948 - val_specificity: 0.9896 - val_miss_rate: 0.5999 - val_fall_out: 0.0104 - val_mcc: 0.5396
Epoch 7/100
63/63 [==============================] - 3s 48ms/step - loss: 1.1494 - accuracy: 0.5813 - recall: 0.4018 - precision: 0.7495 - AUROC: 0.9255 - AUPRC: 0.6530 - f1_score: 0.5232 - balanced_accuracy: 0.6934 - specificity: 0.9851 - miss_rate: 0.5982 - fall_out: 0.0149 - mcc: 0.5153 - val_loss: 1.0489 - val_accuracy: 0.6269 - val_recall: 0.3991 - val_precision: 0.8328 - val_AUROC: 0.9400 - val_AUPRC: 0.7110 - val_f1_score: 0.5396 - val_balanced_accuracy: 0.6951 - val_specificity: 0.9911 - val_miss_rate: 0.6009 - val_fall_out: 0.0089 - val_mcc: 0.5480
Epoch 8/100
63/63 [==============================] - 3s 48ms/step - loss: 1.0737 - accuracy: 0.6185 - recall: 0.4463 - precision: 0.7587 - AUROC: 0.9350 - AUPRC: 0.6891 - f1_score: 0.5620 - balanced_accuracy: 0.7152 - specificity: 0.9842 - miss_rate: 0.5537 - fall_out: 0.0158 - mcc: 0.5489 - val_loss: 0.9399 - val_accuracy: 0.6695 - val_recall: 0.5028 - val_precision: 0.8183 - val_AUROC: 0.9511 - val_AUPRC: 0.7529 - val_f1_score: 0.6228 - val_balanced_accuracy: 0.7452 - val_specificity: 0.9876 - val_miss_rate: 0.4972 - val_fall_out: 0.0124 - val_mcc: 0.6126
Epoch 9/100
63/63 [==============================] - 3s 48ms/step - loss: 0.9855 - accuracy: 0.6554 - recall: 0.5088 - precision: 0.7765 - AUROC: 0.9447 - AUPRC: 0.7318 - f1_score: 0.6148 - balanced_accuracy: 0.7462 - specificity: 0.9837 - miss_rate: 0.4912 - fall_out: 0.0163 - mcc: 0.5971 - val_loss: 0.9484 - val_accuracy: 0.6675 - val_recall: 0.5173 - val_precision: 0.7808 - val_AUROC: 0.9487 - val_AUPRC: 0.7545 - val_f1_score: 0.6223 - val_balanced_accuracy: 0.7506 - val_specificity: 0.9839 - val_miss_rate: 0.4827 - val_fall_out: 0.0161 - val_mcc: 0.6045
Epoch 10/100
63/63 [==============================] - 3s 48ms/step - loss: 0.8897 - accuracy: 0.6930 - recall: 0.5709 - precision: 0.8052 - AUROC: 0.9546 - AUPRC: 0.7741 - f1_score: 0.6681 - balanced_accuracy: 0.7778 - specificity: 0.9846 - miss_rate: 0.4291 - fall_out: 0.0154 - mcc: 0.6493 - val_loss: 0.8619 - val_accuracy: 0.6865 - val_recall: 0.5874 - val_precision: 0.7958 - val_AUROC: 0.9584 - val_AUPRC: 0.7837 - val_f1_score: 0.6759 - val_balanced_accuracy: 0.7853 - val_specificity: 0.9833 - val_miss_rate: 0.4126 - val_fall_out: 0.0167 - val_mcc: 0.6547
Epoch 11/100
63/63 [==============================] - 3s 48ms/step - loss: 0.8332 - accuracy: 0.7136 - recall: 0.6081 - precision: 0.8124 - AUROC: 0.9598 - AUPRC: 0.7968 - f1_score: 0.6956 - balanced_accuracy: 0.7962 - specificity: 0.9844 - miss_rate: 0.3919 - fall_out: 0.0156 - mcc: 0.6755 - val_loss: 0.8841 - val_accuracy: 0.7001 - val_recall: 0.6234 - val_precision: 0.7920 - val_AUROC: 0.9551 - val_AUPRC: 0.7853 - val_f1_score: 0.6977 - val_balanced_accuracy: 0.8026 - val_specificity: 0.9818 - val_miss_rate: 0.3766 - val_fall_out: 0.0182 - val_mcc: 0.6742
Epoch 12/100
63/63 [==============================] - 3s 48ms/step - loss: 0.7536 - accuracy: 0.7399 - recall: 0.6475 - precision: 0.8331 - AUROC: 0.9668 - AUPRC: 0.8282 - f1_score: 0.7287 - balanced_accuracy: 0.8166 - specificity: 0.9856 - miss_rate: 0.3525 - fall_out: 0.0144 - mcc: 0.7094 - val_loss: 0.7631 - val_accuracy: 0.7376 - val_recall: 0.6505 - val_precision: 0.8332 - val_AUROC: 0.9665 - val_AUPRC: 0.8319 - val_f1_score: 0.7306 - val_balanced_accuracy: 0.8180 - val_specificity: 0.9855 - val_miss_rate: 0.3495 - val_fall_out: 0.0145 - val_mcc: 0.7112
Epoch 13/100
63/63 [==============================] - 3s 48ms/step - loss: 0.7019 - accuracy: 0.7632 - recall: 0.6741 - precision: 0.8462 - AUROC: 0.9710 - AUPRC: 0.8475 - f1_score: 0.7504 - balanced_accuracy: 0.8302 - specificity: 0.9864 - miss_rate: 0.3259 - fall_out: 0.0136 - mcc: 0.7318 - val_loss: 0.7791 - val_accuracy: 0.7276 - val_recall: 0.6630 - val_precision: 0.7976 - val_AUROC: 0.9659 - val_AUPRC: 0.8284 - val_f1_score: 0.7241 - val_balanced_accuracy: 0.8221 - val_specificity: 0.9813 - val_miss_rate: 0.3370 - val_fall_out: 0.0187 - val_mcc: 0.7001
Epoch 14/100
63/63 [==============================] - 3s 48ms/step - loss: 0.6308 - accuracy: 0.7871 - recall: 0.7110 - precision: 0.8587 - AUROC: 0.9762 - AUPRC: 0.8714 - f1_score: 0.7779 - balanced_accuracy: 0.8490 - specificity: 0.9870 - miss_rate: 0.2890 - fall_out: 0.0130 - mcc: 0.7599 - val_loss: 0.6824 - val_accuracy: 0.7692 - val_recall: 0.6925 - val_precision: 0.8443 - val_AUROC: 0.9729 - val_AUPRC: 0.8582 - val_f1_score: 0.7609 - val_balanced_accuracy: 0.8392 - val_specificity: 0.9858 - val_miss_rate: 0.3075 - val_fall_out: 0.0142 - val_mcc: 0.7416
Epoch 15/100
63/63 [==============================] - 3s 48ms/step - loss: 0.5916 - accuracy: 0.7992 - recall: 0.7298 - precision: 0.8647 - AUROC: 0.9788 - AUPRC: 0.8840 - f1_score: 0.7916 - balanced_accuracy: 0.8586 - specificity: 0.9873 - miss_rate: 0.2702 - fall_out: 0.0127 - mcc: 0.7739 - val_loss: 0.7635 - val_accuracy: 0.7451 - val_recall: 0.6955 - val_precision: 0.7987 - val_AUROC: 0.9673 - val_AUPRC: 0.8334 - val_f1_score: 0.7436 - val_balanced_accuracy: 0.8380 - val_specificity: 0.9805 - val_miss_rate: 0.3045 - val_fall_out: 0.0195 - val_mcc: 0.7193
Epoch 16/100
63/63 [==============================] - 3s 48ms/step - loss: 0.5071 - accuracy: 0.8265 - recall: 0.7698 - precision: 0.8830 - AUROC: 0.9840 - AUPRC: 0.9109 - f1_score: 0.8225 - balanced_accuracy: 0.8792 - specificity: 0.9887 - miss_rate: 0.2302 - fall_out: 0.0113 - mcc: 0.8066 - val_loss: 0.6937 - val_accuracy: 0.7737 - val_recall: 0.7336 - val_precision: 0.8180 - val_AUROC: 0.9718 - val_AUPRC: 0.8625 - val_f1_score: 0.7735 - val_balanced_accuracy: 0.8577 - val_specificity: 0.9819 - val_miss_rate: 0.2664 - val_fall_out: 0.0181 - val_mcc: 0.7512
250/250 [==============================] - 2s 7ms/step - loss: 0.3946 - accuracy: 0.8593 - recall: 0.8185 - precision: 0.9041 - AUROC: 0.9908 - AUPRC: 0.9423 - f1_score: 0.8592 - balanced_accuracy: 0.9044 - specificity: 0.9904 - miss_rate: 0.1815 - fall_out: 0.0096 - mcc: 0.8457
63/63 [==============================] - 0s 7ms/step - loss: 0.6937 - accuracy: 0.7737 - recall: 0.7336 - precision: 0.8180 - AUROC: 0.9718 - AUPRC: 0.8625 - f1_score: 0.7735 - balanced_accuracy: 0.8577 - specificity: 0.9819 - miss_rate: 0.2664 - fall_out: 0.0181 - mcc: 0.7512
10it [08:51, 53.12s/it]
MLP_mfccs_metrics_estimate = model_metrics_holdout_estimate(CNN_mfccs_3s_metrics, number_of_splits)
print(f"MLP Metrics - {number_of_splits}-holdouts estimate:")
print(f"Accuracy : train - {MLP_mfccs_metrics_estimate['accuracy_train']} -- test - {MLP_mfccs_metrics_estimate['accuracy_test']}")
print(f"AUROC : train - {MLP_mfccs_metrics_estimate['AUROC_train']} -- test - {MLP_mfccs_metrics_estimate['AUROC_test']}")
print(f"AUPRC : train - {MLP_mfccs_metrics_estimate['AUPRC_train']} -- test - {MLP_mfccs_metrics_estimate['AUPRC_test']}")
print("-"*80)
print("MLP - Train history:")
plot_train_history(CNN_mfccs_3s_history)
print("-"*100)
MLP Metrics - 10-holdouts estimate: Accuracy : train - 0.9264278650283814 -- test - 0.7890836238861084 AUROC : train - 0.9966089308261872 -- test - 0.9697807788848877 AUPRC : train - 0.978346186876297 -- test - 0.8678218841552734 -------------------------------------------------------------------------------- MLP - Train history:
----------------------------------------------------------------------------------------------------
A multi-modal neural network is a model capable of jointly represent and exploit the information of both data modalities (audio features and mfccs). To shape this network the layers of a MLP and a CNN are concatenated, each receiving their specific input, the outputs are then combined into a final dense layer and output layer.
The MMNN is here composed by a MLP and a CNN, which are then concatenated and trained as a whole.
To have a statistically sound estimate of an architecture performance, multiple models are built and trained, each with the same architecture, over different portions of the data (holdouts) and, the average performance of those, is considered as an estimate of the overall performance of the architecture.
Here are the functions to align data in the two modalities, build the models, and to build and train the MMNN.
data['labels_3s'] = data['labels_3s'].values.reshape(-1,).tolist()
labels_3s = labels_3s.values.reshape(-1,).tolist()
print(len(data['mfccs']))
print(len(data['labels_3s']))
print(len(data['features_3s']))
print(len(labels_3s))
9981 9981 9980 9980
audio_records = {}
current_genre = 0
count = 0
for x in data['labels_3s']:
if (x != current_genre):
current_genre = x
count = 0
count += 1
audio_records[current_genre] = count
audio_features_records = {}
current_genre = 0
count = 0
for a in labels_3s:
if (a != current_genre):
current_genre = a
count = 0
count += 1
audio_features_records[current_genre] = count
print(audio_records)
print(audio_features_records)
{0: 1000, 1: 998, 2: 997, 3: 999, 4: 998, 5: 990, 6: 1000, 7: 1000, 8: 1000, 9: 999}
{0: 1000, 1: 998, 2: 997, 3: 999, 4: 998, 5: 990, 6: 1000, 7: 1000, 8: 1000, 9: 998}
import dictdiffer
for diff in list(dictdiffer.diff(audio_features_records, audio_records)):
print(f"Data not aligned: {diff}")
Data not aligned: ('change', [9], (998, 999))
data['features_3s'] = data['features_3s'].assign(label=labels_3s)
missing_record_label = 9
print(f"Adding filler record for {missing_record_label} genre")
# Get features of the genre, compute the mean, insert filler row with mean values of the genre
data_genre = data['features_3s'].loc[data['features_3s']['label'] == missing_record_label]
data['features_3s'].loc['fill'] = data_genre.mean(numeric_only=True)
labels_3s.append(missing_record_label)
data['features_3s'].drop(columns='label', inplace=True)
Adding filler record for 9 genre
data['features_3s'].tail()
| chroma_stft_mean | chroma_stft_var | rms_mean | rms_var | spectral_centroid_mean | spectral_centroid_var | spectral_bandwidth_mean | spectral_bandwidth_var | rolloff_mean | rolloff_var | ... | mfcc16_mean | mfcc16_var | mfcc17_mean | mfcc17_var | mfcc18_mean | mfcc18_var | mfcc19_mean | mfcc19_var | mfcc20_mean | mfcc20_var | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 9986 | -0.096986 | -0.221106 | -0.683808 | -0.557025 | -0.334996 | 0.035363 | -0.461796 | 0.089440 | -0.400326 | 0.006542 | ... | 0.082156 | -0.313519 | -1.097868 | 0.753692 | -0.596023 | 0.387686 | -1.481438 | 0.574530 | 0.234605 | -0.670425 |
| 9987 | -0.294935 | 0.346556 | -0.743164 | -0.313275 | -0.798740 | 0.904125 | -0.952597 | 0.445438 | -0.989745 | 0.847088 | ... | -0.335065 | 1.229027 | 0.265369 | -0.647568 | 0.635708 | -0.488365 | 0.716551 | 0.147628 | 0.117531 | -0.071338 |
| 9988 | 0.021098 | -0.026699 | -0.591618 | -0.464542 | -0.116389 | -0.140172 | -0.302428 | -0.611560 | -0.144295 | -0.389084 | ... | 0.359782 | -0.451281 | -0.127506 | -0.790993 | 0.893671 | -0.628180 | 0.809212 | -0.533891 | 0.274300 | -0.825086 |
| 9989 | -0.122802 | 0.145835 | -0.763462 | -0.565311 | -0.532426 | 0.331943 | -0.517406 | 0.271792 | -0.528958 | 0.209712 | ... | -0.016998 | -0.098481 | -0.990300 | 0.520288 | -0.142934 | 0.429189 | -0.672200 | -0.113524 | -0.382814 | -0.329386 |
| fill | 0.021246 | -0.226050 | 0.066743 | -0.057070 | 0.027273 | 0.251956 | 0.044878 | 0.262106 | 0.014553 | 0.263259 | ... | -0.033418 | -0.119369 | -0.295478 | -0.058187 | -0.018038 | 0.006549 | -0.235457 | -0.039161 | -0.042561 | -0.064059 |
5 rows × 57 columns
print(len(data['mfccs']))
print(len(data['features_3s']))
print(len(data['labels_3s']))
print(len(labels_3s))
print(data['labels_3s'] == labels_3s)
9981 9981 9981 9981 True
print(type(data['mfccs']))
print(type(data['labels_3s']))
print(type(labels_3s))
<class 'list'> <class 'list'> <class 'list'>
from keras_mixed_sequence import MixedSequence, VectorSequence
def get_mmnn_sequence(
features: np.ndarray,
mfccs: np.ndarray,
labels: np.ndarray,
batch_size: int = 128
) -> MixedSequence:
""" Returns the feature data and mfccs data for the MMNN training.
Parameters
-------------------------
features: np.ndarray,
The vector with the audio features for MLP.
mfccs: np.ndarray,
The mfccs for CNN.
labels: np.ndarray,
The data labels.
batch_size: int = 128
The number of samples per batch.
Returns
--------------------------
MixedSequence: MixedSequence
MixedSequence object to train the MMNN."""
return MixedSequence(
x={
"features_data": VectorSequence(
features,
batch_size
),
"mfccs_data": VectorSequence(
mfccs,
batch_size
)
},
y=VectorSequence(
labels,
batch_size=batch_size
)
)
def build_fixed_MLP(
input_shape: tuple
) -> tuple[tf.keras.Model, layers.Layer, layers.Layer]:
""" Returns the fixed MLP model for constructing the MMNN.
Parameters
------------------------
input_shape: tuple
The shape of the input.
Returns
------------------------
tuple: tuple[tf.keras.Model, layers.Layer, layers.Layer]:
The fixed MLP model for the MMNN, the input and last hidden layers of the model."""
print("- Building Fixed MLP:\n")
input_features_data = layers.Input(shape=(input_shape[1]), name="features_data")
hidden = layers.Dense(256, activation="relu")(input_features_data)
hidden = layers.Dropout(0.4)(hidden)
hidden = layers.Dense(256, activation="relu")(hidden)
hidden = layers.Dropout(0.4)(hidden)
for _ in range(2):
hidden = layers.Dense(128, activation="relu")(hidden)
hidden = layers.Dropout(0.4)(hidden)
last_hidden_MLP = hidden
output_MLP = layers.Dense(10, activation="softmax")(last_hidden_MLP)
MLP = tf.keras.Model(
inputs = input_features_data,
outputs = output_MLP
)
MLP.compile(
optimizer="nadam",
loss="categorical_crossentropy",
metrics=get_standard_binary_metrics()
)
MLP.summary()
return MLP, input_features_data, last_hidden_MLP
def build_fixed_CNN(
input_shape: tuple
) -> tuple[tf.keras.Model, layers.Layer, layers.Layer]:
""" Returns the fixed CNN model for constructing the MMNN.
Parameters
------------------------
input_shape: tuple
The shape of the input.
Returns
------------------------
tuple: tuple[tf.keras.Model, layers.Layer, layers.Layer]:
The fixed CNN model for the MMNN, the input and last hidden layers of the model."""
print("- Building Fixed CNN:\n")
input_mfccs_data = layers.Input(shape=(input_shape), name="mfccs_data")
hidden = layers.Conv2D(
256,
kernel_size=3,
padding="same",
activation="relu"
)(input_mfccs_data)
hidden = layers.MaxPooling2D(pool_size=(3),strides=(2),padding='same')(hidden)
hidden = layers.Dropout(0.2)(hidden)
hidden = layers.Conv2D(
256,
kernel_size=3,
padding="same",
activation="relu"
)(hidden)
hidden = layers.MaxPooling2D(pool_size=(3),strides=(2),padding='same')(hidden)
hidden = layers.Dropout(0.2)(hidden)
hidden = layers.Conv2D(
256,
kernel_size=3,
padding="same",
activation="relu"
)(hidden)
hidden = layers.MaxPooling2D(pool_size=(3),strides=(2),padding='same')(hidden)
hidden = layers.Dropout(0.2)(hidden)
hidden = layers.Conv2D(
512,
kernel_size=4,
padding="same",
activation="relu"
)(hidden)
hidden = layers.MaxPooling2D(pool_size=(3),strides=(2),padding='same')(hidden)
hidden = layers.Flatten()(hidden)
hidden = layers.Dense(128, activation="relu")(hidden)
hidden = layers.Dropout(0.5)(hidden)
hidden = layers.Dense(128, activation="relu")(hidden)
last_hidden_CNN = layers.Dropout(0.5)(hidden)
output_CNN = layers.Dense(10, activation="softmax")(last_hidden_CNN)
CNN = tf.keras.Model(
inputs = input_mfccs_data,
outputs = output_CNN
)
CNN.compile(
optimizer="adam",
loss="categorical_crossentropy",
metrics=get_standard_binary_metrics()
)
CNN.summary()
return CNN, input_mfccs_data, last_hidden_CNN
def build_MMNN(
input_features_data: layers.Layer,
input_mfccs_data: layers.Layer,
last_hidden_MLP: layers.Layer,
last_hidden_CNN: layers.Layer
) -> tf.keras.Model:
""" Returns the MMNN model obtained concatenating the MLP and the CNN.
Parameters
------------------------
input_features_data: layers.Layer
The audio feature data (MLP input).
input_mfccs_data: layers.Layer
The mfccs data (CNN input).
last_hidden_MLP: layers.Layer
The last hidden layer of the MLP model.
last_hidden_CNN: layers.Layer
The last hidden layer of the CNN model.
Returns
------------------------
MMNN: Model
The MMNN model."""
print("- Building MMNN:\n")
concatenation_layer = layers.Concatenate()([
last_hidden_MLP,
last_hidden_CNN
])
last_hidden_MMNN = layers.Dense(64, activation='relu')(concatenation_layer)
output_MMNN = layers.Dense(10, activation='softmax')(last_hidden_MMNN)
MMNN = tf.keras.Model(
inputs = [input_features_data, input_mfccs_data],
outputs = output_MMNN
)
MMNN.compile(
optimizer='nadam',
loss='categorical_crossentropy',
metrics=get_standard_binary_metrics()
)
MMNN.summary()
return MMNN
def train_MMNN(
model: tf.keras.Model,
x_features_train: np.ndarray,
x_features_test: np.ndarray,
x_mfccs_train: np.ndarray,
x_mfccs_test: np.ndarray,
y_train: np.ndarray,
y_test: np.ndarray,
epochs: int,
batch_size: int
):
""" Train the MMNN model and return the performance metrics (accuracy, AUROC, AUPRC) for train and test
and the training history.
Parameters
------------------------
x_features_train: np.ndarray
The audio feature input data for training.
x_features_test: np.ndarray
The audio feature input data for testing.
x_mfccs_train: np.ndarray
The mfccs input data for training.
x_mfccs_test: np.ndarray
The mfccs input data for testing.
y_train: np.ndarray
The labels of the input data for training.
y_test: np.ndarray
The labels of the input data for testing.
epochs: int
The number of times the learning algorithm works through the dataset.
batch_size: int
The number of samples to work through before updating the model.
Returns
------------------------
MMNN: Model
The performance metrics and history of the MMNN model."""
train_sequence = get_mmnn_sequence(x_features_train, x_mfccs_train, y_train, batch_size)
test_sequence = get_mmnn_sequence(x_features_test, x_mfccs_test, y_test, batch_size)
MMNN_history = model.fit(
train_sequence,
validation_data = test_sequence,
epochs = epochs,
batch_size = batch_size,
callbacks = [EarlyStopping("val_loss", patience=2)]
)
train_evaluation = model.evaluate(train_sequence, return_dict=True)
test_evaluation = model.evaluate(test_sequence, return_dict=True)
MMNN_metrics = {"train_evaluation": train_evaluation, "test_evaluation": test_evaluation}
return MMNN_metrics, MMNN_history
print("---- Multi Modal Neural Network ----")
features_data = data['features_3s']
normalized_input_data = [cmvnw(np.array(x), win_size=301, variance_normalization=True) for x in data['mfccs']]
mfccs_data = [np.expand_dims(x, axis=-1) for x in normalized_input_data]
data_labels = pd.DataFrame(labels_3s)
MMNN_metrics = []
MMNN_history = []
# Generate holdouts
for holdout_number, (train_indices, test_indices) in enumerate(holdouts_generator.split(features_data, data_labels)):
print(f"-- HOLDOUT {holdout_number+1} --")
# Train/Test data
x_features_train, x_features_test = features_data.iloc[train_indices], features_data.iloc[test_indices]
x_mfccs_train, x_mfccs_test = np.array([mfccs_data[x] for x in train_indices]), np.array([mfccs_data[x] for x in test_indices])
y_train, y_test = data_labels.iloc[train_indices], data_labels.iloc[test_indices]
useless_features = []
## Remove uncorrelated features with the output (feature data)
uncorrelated_features = uncorrelated_features_test(x_features_train, y_train)
for feature in (x_features_train.columns):
if feature in (uncorrelated_features):
useless_features.append(feature)
## Remove correlated features with eachother (feature data)
correlated_features = correlated_features_test(x_features_train)
for feature in (x_features_train.columns):
if feature in (correlated_features):
useless_features.append(feature)
# Correlation tests already executed, removing useless features found
print(f"- Removing uncorrelated/correlated features: {useless_features}\n")
for feature in (x_features_train.columns):
if feature in (useless_features):
x_features_train.drop(columns=feature, inplace=True)
x_features_test.drop(columns=feature, inplace=True)
# One-hot encoding
y_train = one_hot_encoding(y_train, 10)
y_test = one_hot_encoding(y_test, 10)
# Build Fixed MLP, CNN
MLP, input_features_data, last_hidden_MLP = build_fixed_MLP(x_features_train.shape)
CNN, input_mfccs_data, last_hidden_CNN = build_fixed_CNN(x_mfccs_train.shape[1:])
# Build MMNN
MMNN = build_MMNN(
input_features_data,
input_mfccs_data,
last_hidden_MLP,
last_hidden_CNN
)
print("- Training MMNN model:\n")
MMNN_holdout_metrics, MMNN_holdout_history = train_MMNN(
MMNN,
x_features_train.values,
x_features_test.values,
np.array(x_mfccs_train),
np.array(x_mfccs_test),
y_train.values,
y_test.values,
epochs,
batch_size
)
MMNN_metrics.append(MMNN_holdout_metrics)
MMNN_history.append(MMNN_holdout_history)
---- Multi Modal Neural Network ----
-- HOLDOUT 1 --
-- 6 Uncorrelated features: [Pearson+Spearman]
['mfcc10_var', 'mfcc17_mean', 'mfcc16_mean', 'mfcc11_var', 'tempo', 'mfcc19_mean']
-- Actually uncorrelated features: [confirmed via MIC]
[]
-- 1 Correlated features: [Pearson]
['spectral_centroid_mean']
- Removing uncorrelated/correlated features: ['spectral_centroid_mean']
- Building Fixed MLP:
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
features_data (InputLayer) [(None, 56)] 0
dense (Dense) (None, 256) 14592
dropout (Dropout) (None, 256) 0
dense_1 (Dense) (None, 256) 65792
dropout_1 (Dropout) (None, 256) 0
dense_2 (Dense) (None, 128) 32896
dropout_2 (Dropout) (None, 128) 0
dense_3 (Dense) (None, 128) 16512
dropout_3 (Dropout) (None, 128) 0
dense_4 (Dense) (None, 10) 1290
=================================================================
Total params: 131,082
Trainable params: 131,082
Non-trainable params: 0
_________________________________________________________________
- Building Fixed CNN:
Model: "model_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
mfccs_data (InputLayer) [(None, 20, 130, 1)] 0
conv2d (Conv2D) (None, 20, 130, 256) 2560
max_pooling2d (MaxPooling2D (None, 10, 65, 256) 0
)
dropout_4 (Dropout) (None, 10, 65, 256) 0
conv2d_1 (Conv2D) (None, 10, 65, 256) 590080
max_pooling2d_1 (MaxPooling (None, 5, 33, 256) 0
2D)
dropout_5 (Dropout) (None, 5, 33, 256) 0
conv2d_2 (Conv2D) (None, 5, 33, 256) 590080
max_pooling2d_2 (MaxPooling (None, 3, 17, 256) 0
2D)
dropout_6 (Dropout) (None, 3, 17, 256) 0
conv2d_3 (Conv2D) (None, 3, 17, 512) 2097664
max_pooling2d_3 (MaxPooling (None, 2, 9, 512) 0
2D)
flatten (Flatten) (None, 9216) 0
dense_5 (Dense) (None, 128) 1179776
dropout_7 (Dropout) (None, 128) 0
dense_6 (Dense) (None, 128) 16512
dropout_8 (Dropout) (None, 128) 0
dense_7 (Dense) (None, 10) 1290
=================================================================
Total params: 4,477,962
Trainable params: 4,477,962
Non-trainable params: 0
_________________________________________________________________
C:\Users\Paolo\anaconda3\lib\site-packages\pandas\core\frame.py:4906: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
- Building MMNN:
Model: "model_2"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
mfccs_data (InputLayer) [(None, 20, 130, 1) 0 []
]
conv2d (Conv2D) (None, 20, 130, 256 2560 ['mfccs_data[0][0]']
)
max_pooling2d (MaxPooling2D) (None, 10, 65, 256) 0 ['conv2d[0][0]']
dropout_4 (Dropout) (None, 10, 65, 256) 0 ['max_pooling2d[0][0]']
conv2d_1 (Conv2D) (None, 10, 65, 256) 590080 ['dropout_4[0][0]']
max_pooling2d_1 (MaxPooling2D) (None, 5, 33, 256) 0 ['conv2d_1[0][0]']
dropout_5 (Dropout) (None, 5, 33, 256) 0 ['max_pooling2d_1[0][0]']
conv2d_2 (Conv2D) (None, 5, 33, 256) 590080 ['dropout_5[0][0]']
features_data (InputLayer) [(None, 56)] 0 []
max_pooling2d_2 (MaxPooling2D) (None, 3, 17, 256) 0 ['conv2d_2[0][0]']
dense (Dense) (None, 256) 14592 ['features_data[0][0]']
dropout_6 (Dropout) (None, 3, 17, 256) 0 ['max_pooling2d_2[0][0]']
dropout (Dropout) (None, 256) 0 ['dense[0][0]']
conv2d_3 (Conv2D) (None, 3, 17, 512) 2097664 ['dropout_6[0][0]']
dense_1 (Dense) (None, 256) 65792 ['dropout[0][0]']
max_pooling2d_3 (MaxPooling2D) (None, 2, 9, 512) 0 ['conv2d_3[0][0]']
dropout_1 (Dropout) (None, 256) 0 ['dense_1[0][0]']
flatten (Flatten) (None, 9216) 0 ['max_pooling2d_3[0][0]']
dense_2 (Dense) (None, 128) 32896 ['dropout_1[0][0]']
dense_5 (Dense) (None, 128) 1179776 ['flatten[0][0]']
dropout_2 (Dropout) (None, 128) 0 ['dense_2[0][0]']
dropout_7 (Dropout) (None, 128) 0 ['dense_5[0][0]']
dense_3 (Dense) (None, 128) 16512 ['dropout_2[0][0]']
dense_6 (Dense) (None, 128) 16512 ['dropout_7[0][0]']
dropout_3 (Dropout) (None, 128) 0 ['dense_3[0][0]']
dropout_8 (Dropout) (None, 128) 0 ['dense_6[0][0]']
concatenate (Concatenate) (None, 256) 0 ['dropout_3[0][0]',
'dropout_8[0][0]']
dense_8 (Dense) (None, 64) 16448 ['concatenate[0][0]']
dense_9 (Dense) (None, 10) 650 ['dense_8[0][0]']
==================================================================================================
Total params: 4,623,562
Trainable params: 4,623,562
Non-trainable params: 0
__________________________________________________________________________________________________
- Training MMNN model:
Epoch 1/100
63/63 [==============================] - 9s 113ms/step - loss: 2.1460 - accuracy: 0.2193 - recall: 0.0371 - precision: 0.5725 - AUROC: 0.6719 - AUPRC: 0.2157 - f1_score: 0.0696 - balanced_accuracy: 0.5170 - specificity: 0.9969 - miss_rate: 0.9629 - fall_out: 0.0031 - mcc: 0.1272 - val_loss: 1.6747 - val_accuracy: 0.3756 - val_recall: 0.1532 - val_precision: 0.7556 - val_AUROC: 0.8383 - val_AUPRC: 0.4273 - val_f1_score: 0.2548 - val_balanced_accuracy: 0.5739 - val_specificity: 0.9945 - val_miss_rate: 0.8468 - val_fall_out: 0.0055 - val_mcc: 0.3144
Epoch 2/100
63/63 [==============================] - 7s 104ms/step - loss: 1.7147 - accuracy: 0.3696 - recall: 0.1438 - precision: 0.6142 - AUROC: 0.8276 - AUPRC: 0.3833 - f1_score: 0.2330 - balanced_accuracy: 0.5669 - specificity: 0.9900 - miss_rate: 0.8562 - fall_out: 0.0100 - mcc: 0.2654 - val_loss: 1.4536 - val_accuracy: 0.4552 - val_recall: 0.2419 - val_precision: 0.6960 - val_AUROC: 0.8847 - val_AUPRC: 0.5193 - val_f1_score: 0.3590 - val_balanced_accuracy: 0.6151 - val_specificity: 0.9883 - val_miss_rate: 0.7581 - val_fall_out: 0.0117 - val_mcc: 0.3769
Epoch 3/100
63/63 [==============================] - 7s 102ms/step - loss: 1.5105 - accuracy: 0.4390 - recall: 0.2132 - precision: 0.6521 - AUROC: 0.8701 - AUPRC: 0.4700 - f1_score: 0.3213 - balanced_accuracy: 0.6003 - specificity: 0.9874 - miss_rate: 0.7868 - fall_out: 0.0126 - mcc: 0.3383 - val_loss: 1.2921 - val_accuracy: 0.5193 - val_recall: 0.2859 - val_precision: 0.7358 - val_AUROC: 0.9113 - val_AUPRC: 0.5913 - val_f1_score: 0.4118 - val_balanced_accuracy: 0.6373 - val_specificity: 0.9886 - val_miss_rate: 0.7141 - val_fall_out: 0.0114 - val_mcc: 0.4262
Epoch 4/100
63/63 [==============================] - 7s 103ms/step - loss: 1.3587 - accuracy: 0.5058 - recall: 0.2839 - precision: 0.6909 - AUROC: 0.8963 - AUPRC: 0.5408 - f1_score: 0.4025 - balanced_accuracy: 0.6349 - specificity: 0.9859 - miss_rate: 0.7161 - fall_out: 0.0141 - mcc: 0.4078 - val_loss: 1.1344 - val_accuracy: 0.5679 - val_recall: 0.3585 - val_precision: 0.7724 - val_AUROC: 0.9331 - val_AUPRC: 0.6681 - val_f1_score: 0.4897 - val_balanced_accuracy: 0.6734 - val_specificity: 0.9883 - val_miss_rate: 0.6415 - val_fall_out: 0.0117 - val_mcc: 0.4945
Epoch 5/100
63/63 [==============================] - 7s 100ms/step - loss: 1.2208 - accuracy: 0.5718 - recall: 0.3727 - precision: 0.7368 - AUROC: 0.9164 - AUPRC: 0.6199 - f1_score: 0.4951 - balanced_accuracy: 0.6790 - specificity: 0.9852 - miss_rate: 0.6273 - fall_out: 0.0148 - mcc: 0.4900 - val_loss: 0.9561 - val_accuracy: 0.6810 - val_recall: 0.4752 - val_precision: 0.8083 - val_AUROC: 0.9505 - val_AUPRC: 0.7509 - val_f1_score: 0.5985 - val_balanced_accuracy: 0.7313 - val_specificity: 0.9875 - val_miss_rate: 0.5248 - val_fall_out: 0.0125 - val_mcc: 0.5901
Epoch 6/100
63/63 [==============================] - 7s 100ms/step - loss: 1.1333 - accuracy: 0.5999 - recall: 0.4222 - precision: 0.7407 - AUROC: 0.9279 - AUPRC: 0.6578 - f1_score: 0.5379 - balanced_accuracy: 0.7029 - specificity: 0.9836 - miss_rate: 0.5778 - fall_out: 0.0164 - mcc: 0.5251 - val_loss: 0.9138 - val_accuracy: 0.6965 - val_recall: 0.5033 - val_precision: 0.8375 - val_AUROC: 0.9562 - val_AUPRC: 0.7732 - val_f1_score: 0.6287 - val_balanced_accuracy: 0.7462 - val_specificity: 0.9892 - val_miss_rate: 0.4967 - val_fall_out: 0.0108 - val_mcc: 0.6216
Epoch 7/100
63/63 [==============================] - 7s 100ms/step - loss: 1.0396 - accuracy: 0.6355 - recall: 0.4905 - precision: 0.7669 - AUROC: 0.9390 - AUPRC: 0.7048 - f1_score: 0.5983 - balanced_accuracy: 0.7370 - specificity: 0.9834 - miss_rate: 0.5095 - fall_out: 0.0166 - mcc: 0.5811 - val_loss: 0.8414 - val_accuracy: 0.7141 - val_recall: 0.5603 - val_precision: 0.8332 - val_AUROC: 0.9618 - val_AUPRC: 0.8001 - val_f1_score: 0.6701 - val_balanced_accuracy: 0.7739 - val_specificity: 0.9875 - val_miss_rate: 0.4397 - val_fall_out: 0.0125 - val_mcc: 0.6563
Epoch 8/100
63/63 [==============================] - 7s 100ms/step - loss: 0.9664 - accuracy: 0.6672 - recall: 0.5282 - precision: 0.7776 - AUROC: 0.9469 - AUPRC: 0.7331 - f1_score: 0.6291 - balanced_accuracy: 0.7557 - specificity: 0.9832 - miss_rate: 0.4718 - fall_out: 0.0168 - mcc: 0.6097 - val_loss: 0.7755 - val_accuracy: 0.7376 - val_recall: 0.6139 - val_precision: 0.8323 - val_AUROC: 0.9664 - val_AUPRC: 0.8239 - val_f1_score: 0.7066 - val_balanced_accuracy: 0.8001 - val_specificity: 0.9863 - val_miss_rate: 0.3861 - val_fall_out: 0.0137 - val_mcc: 0.6889
Epoch 9/100
63/63 [==============================] - 7s 100ms/step - loss: 0.9052 - accuracy: 0.6869 - recall: 0.5696 - precision: 0.7892 - AUROC: 0.9531 - AUPRC: 0.7630 - f1_score: 0.6617 - balanced_accuracy: 0.7764 - specificity: 0.9831 - miss_rate: 0.4304 - fall_out: 0.0169 - mcc: 0.6408 - val_loss: 0.7497 - val_accuracy: 0.7456 - val_recall: 0.6309 - val_precision: 0.8246 - val_AUROC: 0.9677 - val_AUPRC: 0.8320 - val_f1_score: 0.7149 - val_balanced_accuracy: 0.8080 - val_specificity: 0.9851 - val_miss_rate: 0.3691 - val_fall_out: 0.0149 - val_mcc: 0.6952
Epoch 10/100
63/63 [==============================] - 7s 99ms/step - loss: 0.8771 - accuracy: 0.7009 - recall: 0.5917 - precision: 0.8061 - AUROC: 0.9552 - AUPRC: 0.7795 - f1_score: 0.6825 - balanced_accuracy: 0.7879 - specificity: 0.9842 - miss_rate: 0.4083 - fall_out: 0.0158 - mcc: 0.6625 - val_loss: 0.7129 - val_accuracy: 0.7611 - val_recall: 0.6520 - val_precision: 0.8455 - val_AUROC: 0.9714 - val_AUPRC: 0.8467 - val_f1_score: 0.7362 - val_balanced_accuracy: 0.8194 - val_specificity: 0.9868 - val_miss_rate: 0.3480 - val_fall_out: 0.0132 - val_mcc: 0.7183
Epoch 11/100
63/63 [==============================] - 7s 99ms/step - loss: 0.8079 - accuracy: 0.7249 - recall: 0.6235 - precision: 0.8115 - AUROC: 0.9619 - AUPRC: 0.8047 - f1_score: 0.7052 - balanced_accuracy: 0.8037 - specificity: 0.9839 - miss_rate: 0.3765 - fall_out: 0.0161 - mcc: 0.6842 - val_loss: 0.6790 - val_accuracy: 0.7742 - val_recall: 0.6685 - val_precision: 0.8558 - val_AUROC: 0.9739 - val_AUPRC: 0.8596 - val_f1_score: 0.7506 - val_balanced_accuracy: 0.8280 - val_specificity: 0.9875 - val_miss_rate: 0.3315 - val_fall_out: 0.0125 - val_mcc: 0.7333
Epoch 12/100
63/63 [==============================] - 7s 100ms/step - loss: 0.7566 - accuracy: 0.7429 - recall: 0.6532 - precision: 0.8232 - AUROC: 0.9666 - AUPRC: 0.8253 - f1_score: 0.7284 - balanced_accuracy: 0.8188 - specificity: 0.9844 - miss_rate: 0.3468 - fall_out: 0.0156 - mcc: 0.7077 - val_loss: 0.6464 - val_accuracy: 0.7902 - val_recall: 0.6980 - val_precision: 0.8542 - val_AUROC: 0.9753 - val_AUPRC: 0.8692 - val_f1_score: 0.7683 - val_balanced_accuracy: 0.8424 - val_specificity: 0.9868 - val_miss_rate: 0.3020 - val_fall_out: 0.0132 - val_mcc: 0.7499
Epoch 13/100
63/63 [==============================] - 7s 101ms/step - loss: 0.7031 - accuracy: 0.7645 - recall: 0.6847 - precision: 0.8380 - AUROC: 0.9706 - AUPRC: 0.8436 - f1_score: 0.7537 - balanced_accuracy: 0.8350 - specificity: 0.9853 - miss_rate: 0.3153 - fall_out: 0.0147 - mcc: 0.7338 - val_loss: 0.5933 - val_accuracy: 0.7912 - val_recall: 0.7311 - val_precision: 0.8614 - val_AUROC: 0.9782 - val_AUPRC: 0.8880 - val_f1_score: 0.7909 - val_balanced_accuracy: 0.8590 - val_specificity: 0.9869 - val_miss_rate: 0.2689 - val_fall_out: 0.0131 - val_mcc: 0.7729
Epoch 14/100
63/63 [==============================] - 7s 101ms/step - loss: 0.6615 - accuracy: 0.7737 - recall: 0.7079 - precision: 0.8423 - AUROC: 0.9736 - AUPRC: 0.8592 - f1_score: 0.7693 - balanced_accuracy: 0.8466 - specificity: 0.9853 - miss_rate: 0.2921 - fall_out: 0.0147 - mcc: 0.7495 - val_loss: 0.5633 - val_accuracy: 0.8062 - val_recall: 0.7511 - val_precision: 0.8686 - val_AUROC: 0.9802 - val_AUPRC: 0.8959 - val_f1_score: 0.8056 - val_balanced_accuracy: 0.8692 - val_specificity: 0.9874 - val_miss_rate: 0.2489 - val_fall_out: 0.0126 - val_mcc: 0.7882
Epoch 15/100
63/63 [==============================] - 7s 100ms/step - loss: 0.6134 - accuracy: 0.7956 - recall: 0.7311 - precision: 0.8552 - AUROC: 0.9767 - AUPRC: 0.8762 - f1_score: 0.7883 - balanced_accuracy: 0.8587 - specificity: 0.9863 - miss_rate: 0.2689 - fall_out: 0.0137 - mcc: 0.7697 - val_loss: 0.5639 - val_accuracy: 0.8162 - val_recall: 0.7616 - val_precision: 0.8682 - val_AUROC: 0.9799 - val_AUPRC: 0.8944 - val_f1_score: 0.8114 - val_balanced_accuracy: 0.8744 - val_specificity: 0.9871 - val_miss_rate: 0.2384 - val_fall_out: 0.0129 - val_mcc: 0.7940
Epoch 16/100
63/63 [==============================] - 7s 100ms/step - loss: 0.7075 - accuracy: 0.7700 - recall: 0.7002 - precision: 0.8370 - AUROC: 0.9695 - AUPRC: 0.8491 - f1_score: 0.7625 - balanced_accuracy: 0.8425 - specificity: 0.9848 - miss_rate: 0.2998 - fall_out: 0.0152 - mcc: 0.7422 - val_loss: 0.5555 - val_accuracy: 0.8162 - val_recall: 0.7496 - val_precision: 0.8683 - val_AUROC: 0.9805 - val_AUPRC: 0.8996 - val_f1_score: 0.8046 - val_balanced_accuracy: 0.8685 - val_specificity: 0.9874 - val_miss_rate: 0.2504 - val_fall_out: 0.0126 - val_mcc: 0.7872
Epoch 17/100
63/63 [==============================] - 7s 100ms/step - loss: 0.6091 - accuracy: 0.7987 - recall: 0.7387 - precision: 0.8552 - AUROC: 0.9773 - AUPRC: 0.8761 - f1_score: 0.7927 - balanced_accuracy: 0.8624 - specificity: 0.9861 - miss_rate: 0.2613 - fall_out: 0.0139 - mcc: 0.7740 - val_loss: 0.5199 - val_accuracy: 0.8338 - val_recall: 0.7827 - val_precision: 0.8836 - val_AUROC: 0.9830 - val_AUPRC: 0.9087 - val_f1_score: 0.8301 - val_balanced_accuracy: 0.8856 - val_specificity: 0.9885 - val_miss_rate: 0.2173 - val_fall_out: 0.0115 - val_mcc: 0.8143
Epoch 18/100
63/63 [==============================] - 7s 100ms/step - loss: 0.5555 - accuracy: 0.8161 - recall: 0.7619 - precision: 0.8684 - AUROC: 0.9808 - AUPRC: 0.8932 - f1_score: 0.8117 - balanced_accuracy: 0.8745 - specificity: 0.9872 - miss_rate: 0.2381 - fall_out: 0.0128 - mcc: 0.7943 - val_loss: 0.4881 - val_accuracy: 0.8393 - val_recall: 0.7917 - val_precision: 0.8877 - val_AUROC: 0.9844 - val_AUPRC: 0.9179 - val_f1_score: 0.8370 - val_balanced_accuracy: 0.8903 - val_specificity: 0.9889 - val_miss_rate: 0.2083 - val_fall_out: 0.0111 - val_mcc: 0.8216
Epoch 19/100
63/63 [==============================] - 7s 100ms/step - loss: 0.5108 - accuracy: 0.8309 - recall: 0.7817 - precision: 0.8761 - AUROC: 0.9832 - AUPRC: 0.9067 - f1_score: 0.8262 - balanced_accuracy: 0.8847 - specificity: 0.9877 - miss_rate: 0.2183 - fall_out: 0.0123 - mcc: 0.8097 - val_loss: 0.4954 - val_accuracy: 0.8343 - val_recall: 0.8072 - val_precision: 0.8709 - val_AUROC: 0.9844 - val_AUPRC: 0.9175 - val_f1_score: 0.8378 - val_balanced_accuracy: 0.8970 - val_specificity: 0.9867 - val_miss_rate: 0.1928 - val_fall_out: 0.0133 - val_mcc: 0.8213
Epoch 20/100
63/63 [==============================] - 7s 100ms/step - loss: 0.4782 - accuracy: 0.8379 - recall: 0.7970 - precision: 0.8786 - AUROC: 0.9857 - AUPRC: 0.9157 - f1_score: 0.8358 - balanced_accuracy: 0.8924 - specificity: 0.9878 - miss_rate: 0.2030 - fall_out: 0.0122 - mcc: 0.8197 - val_loss: 0.4621 - val_accuracy: 0.8533 - val_recall: 0.8132 - val_precision: 0.8904 - val_AUROC: 0.9860 - val_AUPRC: 0.9253 - val_f1_score: 0.8500 - val_balanced_accuracy: 0.9010 - val_specificity: 0.9889 - val_miss_rate: 0.1868 - val_fall_out: 0.0111 - val_mcc: 0.8353
Epoch 21/100
63/63 [==============================] - 7s 101ms/step - loss: 0.4613 - accuracy: 0.8483 - recall: 0.8130 - precision: 0.8930 - AUROC: 0.9861 - AUPRC: 0.9233 - f1_score: 0.8511 - balanced_accuracy: 0.9011 - specificity: 0.9892 - miss_rate: 0.1870 - fall_out: 0.0108 - mcc: 0.8365 - val_loss: 0.4543 - val_accuracy: 0.8603 - val_recall: 0.8277 - val_precision: 0.8911 - val_AUROC: 0.9854 - val_AUPRC: 0.9266 - val_f1_score: 0.8583 - val_balanced_accuracy: 0.9083 - val_specificity: 0.9888 - val_miss_rate: 0.1723 - val_fall_out: 0.0112 - val_mcc: 0.8439
Epoch 22/100
63/63 [==============================] - 7s 100ms/step - loss: 0.4358 - accuracy: 0.8601 - recall: 0.8258 - precision: 0.8953 - AUROC: 0.9873 - AUPRC: 0.9287 - f1_score: 0.8591 - balanced_accuracy: 0.9075 - specificity: 0.9893 - miss_rate: 0.1742 - fall_out: 0.0107 - mcc: 0.8450 - val_loss: 0.4309 - val_accuracy: 0.8588 - val_recall: 0.8267 - val_precision: 0.8895 - val_AUROC: 0.9880 - val_AUPRC: 0.9338 - val_f1_score: 0.8570 - val_balanced_accuracy: 0.9077 - val_specificity: 0.9886 - val_miss_rate: 0.1733 - val_fall_out: 0.0114 - val_mcc: 0.8424
Epoch 23/100
63/63 [==============================] - 7s 100ms/step - loss: 0.4108 - accuracy: 0.8637 - recall: 0.8303 - precision: 0.8984 - AUROC: 0.9884 - AUPRC: 0.9358 - f1_score: 0.8630 - balanced_accuracy: 0.9099 - specificity: 0.9896 - miss_rate: 0.1697 - fall_out: 0.0104 - mcc: 0.8492 - val_loss: 0.3946 - val_accuracy: 0.8773 - val_recall: 0.8473 - val_precision: 0.9111 - val_AUROC: 0.9888 - val_AUPRC: 0.9425 - val_f1_score: 0.8780 - val_balanced_accuracy: 0.9190 - val_specificity: 0.9908 - val_miss_rate: 0.1527 - val_fall_out: 0.0092 - val_mcc: 0.8657
Epoch 24/100
63/63 [==============================] - 7s 100ms/step - loss: 0.3813 - accuracy: 0.8711 - recall: 0.8391 - precision: 0.8998 - AUROC: 0.9904 - AUPRC: 0.9427 - f1_score: 0.8684 - balanced_accuracy: 0.9143 - specificity: 0.9896 - miss_rate: 0.1609 - fall_out: 0.0104 - mcc: 0.8549 - val_loss: 0.4948 - val_accuracy: 0.8488 - val_recall: 0.8272 - val_precision: 0.8769 - val_AUROC: 0.9820 - val_AUPRC: 0.9189 - val_f1_score: 0.8513 - val_balanced_accuracy: 0.9072 - val_specificity: 0.9871 - val_miss_rate: 0.1728 - val_fall_out: 0.0129 - val_mcc: 0.8358
Epoch 25/100
63/63 [==============================] - 7s 100ms/step - loss: 0.3572 - accuracy: 0.8846 - recall: 0.8562 - precision: 0.9133 - AUROC: 0.9905 - AUPRC: 0.9473 - f1_score: 0.8838 - balanced_accuracy: 0.9236 - specificity: 0.9910 - miss_rate: 0.1438 - fall_out: 0.0090 - mcc: 0.8719 - val_loss: 0.4303 - val_accuracy: 0.8663 - val_recall: 0.8443 - val_precision: 0.8883 - val_AUROC: 0.9867 - val_AUPRC: 0.9346 - val_f1_score: 0.8657 - val_balanced_accuracy: 0.9162 - val_specificity: 0.9882 - val_miss_rate: 0.1557 - val_fall_out: 0.0118 - val_mcc: 0.8516
63/63 [==============================] - 4s 56ms/step - loss: 0.1498 - accuracy: 0.9572 - recall: 0.9414 - precision: 0.9701 - AUROC: 0.9989 - AUPRC: 0.9916 - f1_score: 0.9555 - balanced_accuracy: 0.9691 - specificity: 0.9968 - miss_rate: 0.0586 - fall_out: 0.0032 - mcc: 0.9508
16/16 [==============================] - 3s 164ms/step - loss: 0.4303 - accuracy: 0.8663 - recall: 0.8443 - precision: 0.8883 - AUROC: 0.9867 - AUPRC: 0.9346 - f1_score: 0.8657 - balanced_accuracy: 0.9162 - specificity: 0.9882 - miss_rate: 0.1557 - fall_out: 0.0118 - mcc: 0.8516
-- HOLDOUT 2 --
-- 6 Uncorrelated features: [Pearson+Spearman]
['mfcc10_var', 'mfcc17_mean', 'mfcc11_var', 'mfcc16_mean', 'tempo', 'mfcc19_mean']
-- Actually uncorrelated features: [confirmed via MIC]
[]
-- 1 Correlated features: [Pearson]
['spectral_centroid_mean']
- Removing uncorrelated/correlated features: ['spectral_centroid_mean']
- Building Fixed MLP:
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
features_data (InputLayer) [(None, 56)] 0
dense (Dense) (None, 256) 14592
dropout (Dropout) (None, 256) 0
dense_1 (Dense) (None, 256) 65792
dropout_1 (Dropout) (None, 256) 0
dense_2 (Dense) (None, 128) 32896
dropout_2 (Dropout) (None, 128) 0
dense_3 (Dense) (None, 128) 16512
dropout_3 (Dropout) (None, 128) 0
dense_4 (Dense) (None, 10) 1290
=================================================================
Total params: 131,082
Trainable params: 131,082
Non-trainable params: 0
_________________________________________________________________
- Building Fixed CNN:
Model: "model_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
mfccs_data (InputLayer) [(None, 20, 130, 1)] 0
conv2d (Conv2D) (None, 20, 130, 256) 2560
max_pooling2d (MaxPooling2D (None, 10, 65, 256) 0
)
dropout_4 (Dropout) (None, 10, 65, 256) 0
conv2d_1 (Conv2D) (None, 10, 65, 256) 590080
max_pooling2d_1 (MaxPooling (None, 5, 33, 256) 0
2D)
dropout_5 (Dropout) (None, 5, 33, 256) 0
conv2d_2 (Conv2D) (None, 5, 33, 256) 590080
max_pooling2d_2 (MaxPooling (None, 3, 17, 256) 0
2D)
dropout_6 (Dropout) (None, 3, 17, 256) 0
conv2d_3 (Conv2D) (None, 3, 17, 512) 2097664
max_pooling2d_3 (MaxPooling (None, 2, 9, 512) 0
2D)
flatten (Flatten) (None, 9216) 0
dense_5 (Dense) (None, 128) 1179776
dropout_7 (Dropout) (None, 128) 0
dense_6 (Dense) (None, 128) 16512
dropout_8 (Dropout) (None, 128) 0
dense_7 (Dense) (None, 10) 1290
=================================================================
Total params: 4,477,962
Trainable params: 4,477,962
Non-trainable params: 0
_________________________________________________________________
- Building MMNN:
Model: "model_2"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
mfccs_data (InputLayer) [(None, 20, 130, 1) 0 []
]
conv2d (Conv2D) (None, 20, 130, 256 2560 ['mfccs_data[0][0]']
)
max_pooling2d (MaxPooling2D) (None, 10, 65, 256) 0 ['conv2d[0][0]']
dropout_4 (Dropout) (None, 10, 65, 256) 0 ['max_pooling2d[0][0]']
conv2d_1 (Conv2D) (None, 10, 65, 256) 590080 ['dropout_4[0][0]']
max_pooling2d_1 (MaxPooling2D) (None, 5, 33, 256) 0 ['conv2d_1[0][0]']
dropout_5 (Dropout) (None, 5, 33, 256) 0 ['max_pooling2d_1[0][0]']
conv2d_2 (Conv2D) (None, 5, 33, 256) 590080 ['dropout_5[0][0]']
features_data (InputLayer) [(None, 56)] 0 []
max_pooling2d_2 (MaxPooling2D) (None, 3, 17, 256) 0 ['conv2d_2[0][0]']
dense (Dense) (None, 256) 14592 ['features_data[0][0]']
dropout_6 (Dropout) (None, 3, 17, 256) 0 ['max_pooling2d_2[0][0]']
dropout (Dropout) (None, 256) 0 ['dense[0][0]']
conv2d_3 (Conv2D) (None, 3, 17, 512) 2097664 ['dropout_6[0][0]']
dense_1 (Dense) (None, 256) 65792 ['dropout[0][0]']
max_pooling2d_3 (MaxPooling2D) (None, 2, 9, 512) 0 ['conv2d_3[0][0]']
dropout_1 (Dropout) (None, 256) 0 ['dense_1[0][0]']
flatten (Flatten) (None, 9216) 0 ['max_pooling2d_3[0][0]']
dense_2 (Dense) (None, 128) 32896 ['dropout_1[0][0]']
dense_5 (Dense) (None, 128) 1179776 ['flatten[0][0]']
dropout_2 (Dropout) (None, 128) 0 ['dense_2[0][0]']
dropout_7 (Dropout) (None, 128) 0 ['dense_5[0][0]']
dense_3 (Dense) (None, 128) 16512 ['dropout_2[0][0]']
dense_6 (Dense) (None, 128) 16512 ['dropout_7[0][0]']
dropout_3 (Dropout) (None, 128) 0 ['dense_3[0][0]']
dropout_8 (Dropout) (None, 128) 0 ['dense_6[0][0]']
concatenate (Concatenate) (None, 256) 0 ['dropout_3[0][0]',
'dropout_8[0][0]']
dense_8 (Dense) (None, 64) 16448 ['concatenate[0][0]']
dense_9 (Dense) (None, 10) 650 ['dense_8[0][0]']
==================================================================================================
Total params: 4,623,562
Trainable params: 4,623,562
Non-trainable params: 0
__________________________________________________________________________________________________
- Training MMNN model:
Epoch 1/100
63/63 [==============================] - 9s 107ms/step - loss: 2.1583 - accuracy: 0.2273 - recall: 0.0445 - precision: 0.5530 - AUROC: 0.6630 - AUPRC: 0.2150 - f1_score: 0.0823 - balanced_accuracy: 0.5202 - specificity: 0.9960 - miss_rate: 0.9555 - fall_out: 0.0040 - mcc: 0.1359 - val_loss: 1.8002 - val_accuracy: 0.4176 - val_recall: 0.1012 - val_precision: 0.7372 - val_AUROC: 0.8145 - val_AUPRC: 0.3997 - val_f1_score: 0.1779 - val_balanced_accuracy: 0.5486 - val_specificity: 0.9960 - val_miss_rate: 0.8988 - val_fall_out: 0.0040 - val_mcc: 0.2505
Epoch 2/100
63/63 [==============================] - 7s 101ms/step - loss: 1.6972 - accuracy: 0.3846 - recall: 0.1700 - precision: 0.6540 - AUROC: 0.8305 - AUPRC: 0.4097 - f1_score: 0.2698 - balanced_accuracy: 0.5800 - specificity: 0.9900 - miss_rate: 0.8300 - fall_out: 0.0100 - mcc: 0.3016 - val_loss: 1.3930 - val_accuracy: 0.4702 - val_recall: 0.2644 - val_precision: 0.7811 - val_AUROC: 0.8967 - val_AUPRC: 0.5585 - val_f1_score: 0.3951 - val_balanced_accuracy: 0.6281 - val_specificity: 0.9918 - val_miss_rate: 0.7356 - val_fall_out: 0.0082 - val_mcc: 0.4249
Epoch 3/100
63/63 [==============================] - 7s 99ms/step - loss: 1.4458 - accuracy: 0.4747 - recall: 0.2499 - precision: 0.6814 - AUROC: 0.8822 - AUPRC: 0.5153 - f1_score: 0.3657 - balanced_accuracy: 0.6184 - specificity: 0.9870 - miss_rate: 0.7501 - fall_out: 0.0130 - mcc: 0.3781 - val_loss: 1.1408 - val_accuracy: 0.5939 - val_recall: 0.3555 - val_precision: 0.8114 - val_AUROC: 0.9318 - val_AUPRC: 0.6776 - val_f1_score: 0.4944 - val_balanced_accuracy: 0.6732 - val_specificity: 0.9908 - val_miss_rate: 0.6445 - val_fall_out: 0.0092 - val_mcc: 0.5076
Epoch 4/100
63/63 [==============================] - 7s 100ms/step - loss: 1.3184 - accuracy: 0.5292 - recall: 0.3174 - precision: 0.7070 - AUROC: 0.9029 - AUPRC: 0.5779 - f1_score: 0.4381 - balanced_accuracy: 0.6514 - specificity: 0.9854 - miss_rate: 0.6826 - fall_out: 0.0146 - mcc: 0.4387 - val_loss: 1.0318 - val_accuracy: 0.6345 - val_recall: 0.4036 - val_precision: 0.8584 - val_AUROC: 0.9448 - val_AUPRC: 0.7322 - val_f1_score: 0.5490 - val_balanced_accuracy: 0.6981 - val_specificity: 0.9926 - val_miss_rate: 0.5964 - val_fall_out: 0.0074 - val_mcc: 0.5615
Epoch 5/100
63/63 [==============================] - 7s 101ms/step - loss: 1.2024 - accuracy: 0.5770 - recall: 0.3808 - precision: 0.7433 - AUROC: 0.9191 - AUPRC: 0.6316 - f1_score: 0.5036 - balanced_accuracy: 0.6831 - specificity: 0.9854 - miss_rate: 0.6192 - fall_out: 0.0146 - mcc: 0.4982 - val_loss: 0.9685 - val_accuracy: 0.6650 - val_recall: 0.4637 - val_precision: 0.8495 - val_AUROC: 0.9499 - val_AUPRC: 0.7532 - val_f1_score: 0.5999 - val_balanced_accuracy: 0.7273 - val_specificity: 0.9909 - val_miss_rate: 0.5363 - val_fall_out: 0.0091 - val_mcc: 0.6003
Epoch 6/100
63/63 [==============================] - 7s 103ms/step - loss: 1.0995 - accuracy: 0.6122 - recall: 0.4479 - precision: 0.7636 - AUROC: 0.9318 - AUPRC: 0.6826 - f1_score: 0.5646 - balanced_accuracy: 0.7162 - specificity: 0.9846 - miss_rate: 0.5521 - fall_out: 0.0154 - mcc: 0.5522 - val_loss: 0.8673 - val_accuracy: 0.6890 - val_recall: 0.5684 - val_precision: 0.8327 - val_AUROC: 0.9588 - val_AUPRC: 0.7904 - val_f1_score: 0.6756 - val_balanced_accuracy: 0.7778 - val_specificity: 0.9873 - val_miss_rate: 0.4316 - val_fall_out: 0.0127 - val_mcc: 0.6610
Epoch 7/100
63/63 [==============================] - 7s 104ms/step - loss: 1.0323 - accuracy: 0.6454 - recall: 0.4932 - precision: 0.7705 - AUROC: 0.9397 - AUPRC: 0.7109 - f1_score: 0.6015 - balanced_accuracy: 0.7385 - specificity: 0.9837 - miss_rate: 0.5068 - fall_out: 0.0163 - mcc: 0.5845 - val_loss: 0.7886 - val_accuracy: 0.7251 - val_recall: 0.6014 - val_precision: 0.8628 - val_AUROC: 0.9665 - val_AUPRC: 0.8281 - val_f1_score: 0.7088 - val_balanced_accuracy: 0.7954 - val_specificity: 0.9894 - val_miss_rate: 0.3986 - val_fall_out: 0.0106 - val_mcc: 0.6960
Epoch 8/100
63/63 [==============================] - 7s 105ms/step - loss: 0.9853 - accuracy: 0.6606 - recall: 0.5230 - precision: 0.7803 - AUROC: 0.9447 - AUPRC: 0.7318 - f1_score: 0.6263 - balanced_accuracy: 0.7533 - specificity: 0.9836 - miss_rate: 0.4770 - fall_out: 0.0164 - mcc: 0.6078 - val_loss: 0.8420 - val_accuracy: 0.7206 - val_recall: 0.5523 - val_precision: 0.8803 - val_AUROC: 0.9647 - val_AUPRC: 0.8147 - val_f1_score: 0.6788 - val_balanced_accuracy: 0.7720 - val_specificity: 0.9917 - val_miss_rate: 0.4477 - val_fall_out: 0.0083 - val_mcc: 0.6730
Epoch 9/100
63/63 [==============================] - 7s 105ms/step - loss: 0.9095 - accuracy: 0.6860 - recall: 0.5666 - precision: 0.7954 - AUROC: 0.9527 - AUPRC: 0.7645 - f1_score: 0.6618 - balanced_accuracy: 0.7752 - specificity: 0.9838 - miss_rate: 0.4334 - fall_out: 0.0162 - mcc: 0.6420 - val_loss: 0.7297 - val_accuracy: 0.7476 - val_recall: 0.6390 - val_precision: 0.8564 - val_AUROC: 0.9711 - val_AUPRC: 0.8463 - val_f1_score: 0.7319 - val_balanced_accuracy: 0.8135 - val_specificity: 0.9881 - val_miss_rate: 0.3610 - val_fall_out: 0.0119 - val_mcc: 0.7159
Epoch 10/100
63/63 [==============================] - 7s 107ms/step - loss: 0.8407 - accuracy: 0.7102 - recall: 0.6062 - precision: 0.8095 - AUROC: 0.9594 - AUPRC: 0.7938 - f1_score: 0.6933 - balanced_accuracy: 0.7952 - specificity: 0.9841 - miss_rate: 0.3938 - fall_out: 0.0159 - mcc: 0.6729 - val_loss: 0.6886 - val_accuracy: 0.7586 - val_recall: 0.6640 - val_precision: 0.8610 - val_AUROC: 0.9740 - val_AUPRC: 0.8590 - val_f1_score: 0.7498 - val_balanced_accuracy: 0.8260 - val_specificity: 0.9881 - val_miss_rate: 0.3360 - val_fall_out: 0.0119 - val_mcc: 0.7333
Epoch 11/100
63/63 [==============================] - 7s 102ms/step - loss: 0.8002 - accuracy: 0.7341 - recall: 0.6423 - precision: 0.8171 - AUROC: 0.9621 - AUPRC: 0.8091 - f1_score: 0.7192 - balanced_accuracy: 0.8132 - specificity: 0.9840 - miss_rate: 0.3577 - fall_out: 0.0160 - mcc: 0.6982 - val_loss: 0.6374 - val_accuracy: 0.7807 - val_recall: 0.6875 - val_precision: 0.8745 - val_AUROC: 0.9774 - val_AUPRC: 0.8782 - val_f1_score: 0.7698 - val_balanced_accuracy: 0.8383 - val_specificity: 0.9890 - val_miss_rate: 0.3125 - val_fall_out: 0.0110 - val_mcc: 0.7541
Epoch 12/100
63/63 [==============================] - 7s 100ms/step - loss: 0.7538 - accuracy: 0.7469 - recall: 0.6579 - precision: 0.8293 - AUROC: 0.9669 - AUPRC: 0.8295 - f1_score: 0.7338 - balanced_accuracy: 0.8214 - specificity: 0.9850 - miss_rate: 0.3421 - fall_out: 0.0150 - mcc: 0.7136 - val_loss: 0.6373 - val_accuracy: 0.7767 - val_recall: 0.6935 - val_precision: 0.8722 - val_AUROC: 0.9773 - val_AUPRC: 0.8767 - val_f1_score: 0.7727 - val_balanced_accuracy: 0.8411 - val_specificity: 0.9887 - val_miss_rate: 0.3065 - val_fall_out: 0.0113 - val_mcc: 0.7565
Epoch 13/100
63/63 [==============================] - 7s 106ms/step - loss: 0.7349 - accuracy: 0.7503 - recall: 0.6683 - precision: 0.8301 - AUROC: 0.9678 - AUPRC: 0.8347 - f1_score: 0.7405 - balanced_accuracy: 0.8266 - specificity: 0.9848 - miss_rate: 0.3317 - fall_out: 0.0152 - mcc: 0.7202 - val_loss: 0.5868 - val_accuracy: 0.7992 - val_recall: 0.7146 - val_precision: 0.8947 - val_AUROC: 0.9810 - val_AUPRC: 0.8958 - val_f1_score: 0.7945 - val_balanced_accuracy: 0.8526 - val_specificity: 0.9907 - val_miss_rate: 0.2854 - val_fall_out: 0.0093 - val_mcc: 0.7804
Epoch 14/100
63/63 [==============================] - 7s 105ms/step - loss: 0.6766 - accuracy: 0.7735 - recall: 0.7010 - precision: 0.8427 - AUROC: 0.9724 - AUPRC: 0.8541 - f1_score: 0.7653 - balanced_accuracy: 0.8432 - specificity: 0.9855 - miss_rate: 0.2990 - fall_out: 0.0145 - mcc: 0.7457 - val_loss: 0.5584 - val_accuracy: 0.8052 - val_recall: 0.7321 - val_precision: 0.8834 - val_AUROC: 0.9820 - val_AUPRC: 0.9022 - val_f1_score: 0.8007 - val_balanced_accuracy: 0.8607 - val_specificity: 0.9893 - val_miss_rate: 0.2679 - val_fall_out: 0.0107 - val_mcc: 0.7850
Epoch 15/100
63/63 [==============================] - 7s 100ms/step - loss: 0.6247 - accuracy: 0.7897 - recall: 0.7262 - precision: 0.8563 - AUROC: 0.9763 - AUPRC: 0.8727 - f1_score: 0.7859 - balanced_accuracy: 0.8563 - specificity: 0.9865 - miss_rate: 0.2738 - fall_out: 0.0135 - mcc: 0.7674 - val_loss: 0.5659 - val_accuracy: 0.8057 - val_recall: 0.7621 - val_precision: 0.8638 - val_AUROC: 0.9810 - val_AUPRC: 0.8995 - val_f1_score: 0.8098 - val_balanced_accuracy: 0.8744 - val_specificity: 0.9866 - val_miss_rate: 0.2379 - val_fall_out: 0.0134 - val_mcc: 0.7920
Epoch 16/100
63/63 [==============================] - 7s 104ms/step - loss: 0.6075 - accuracy: 0.7963 - recall: 0.7400 - precision: 0.8571 - AUROC: 0.9776 - AUPRC: 0.8789 - f1_score: 0.7942 - balanced_accuracy: 0.8631 - specificity: 0.9863 - miss_rate: 0.2600 - fall_out: 0.0137 - mcc: 0.7758 - val_loss: 0.5311 - val_accuracy: 0.8167 - val_recall: 0.7636 - val_precision: 0.8790 - val_AUROC: 0.9832 - val_AUPRC: 0.9082 - val_f1_score: 0.8173 - val_balanced_accuracy: 0.8760 - val_specificity: 0.9883 - val_miss_rate: 0.2364 - val_fall_out: 0.0117 - val_mcc: 0.8009
Epoch 17/100
63/63 [==============================] - 7s 102ms/step - loss: 0.5774 - accuracy: 0.8032 - recall: 0.7486 - precision: 0.8590 - AUROC: 0.9794 - AUPRC: 0.8875 - f1_score: 0.8000 - balanced_accuracy: 0.8675 - specificity: 0.9863 - miss_rate: 0.2514 - fall_out: 0.0137 - mcc: 0.7817 - val_loss: 0.5025 - val_accuracy: 0.8302 - val_recall: 0.7687 - val_precision: 0.9024 - val_AUROC: 0.9855 - val_AUPRC: 0.9202 - val_f1_score: 0.8302 - val_balanced_accuracy: 0.8797 - val_specificity: 0.9908 - val_miss_rate: 0.2313 - val_fall_out: 0.0092 - val_mcc: 0.8162
Epoch 18/100
63/63 [==============================] - 7s 100ms/step - loss: 0.5417 - accuracy: 0.8159 - recall: 0.7673 - precision: 0.8704 - AUROC: 0.9820 - AUPRC: 0.8989 - f1_score: 0.8156 - balanced_accuracy: 0.8773 - specificity: 0.9873 - miss_rate: 0.2327 - fall_out: 0.0127 - mcc: 0.7985 - val_loss: 0.4787 - val_accuracy: 0.8383 - val_recall: 0.7857 - val_precision: 0.8966 - val_AUROC: 0.9860 - val_AUPRC: 0.9261 - val_f1_score: 0.8375 - val_balanced_accuracy: 0.8878 - val_specificity: 0.9899 - val_miss_rate: 0.2143 - val_fall_out: 0.0101 - val_mcc: 0.8229
Epoch 19/100
63/63 [==============================] - 7s 101ms/step - loss: 0.5129 - accuracy: 0.8275 - recall: 0.7806 - precision: 0.8739 - AUROC: 0.9830 - AUPRC: 0.9079 - f1_score: 0.8246 - balanced_accuracy: 0.8840 - specificity: 0.9875 - miss_rate: 0.2194 - fall_out: 0.0125 - mcc: 0.8079 - val_loss: 0.4607 - val_accuracy: 0.8558 - val_recall: 0.8057 - val_precision: 0.8984 - val_AUROC: 0.9866 - val_AUPRC: 0.9292 - val_f1_score: 0.8495 - val_balanced_accuracy: 0.8978 - val_specificity: 0.9899 - val_miss_rate: 0.1943 - val_fall_out: 0.0101 - val_mcc: 0.8353
Epoch 20/100
63/63 [==============================] - 7s 100ms/step - loss: 0.4763 - accuracy: 0.8413 - recall: 0.7980 - precision: 0.8838 - AUROC: 0.9851 - AUPRC: 0.9178 - f1_score: 0.8387 - balanced_accuracy: 0.8932 - specificity: 0.9883 - miss_rate: 0.2020 - fall_out: 0.0117 - mcc: 0.8231 - val_loss: 0.4463 - val_accuracy: 0.8488 - val_recall: 0.8072 - val_precision: 0.8961 - val_AUROC: 0.9872 - val_AUPRC: 0.9336 - val_f1_score: 0.8493 - val_balanced_accuracy: 0.8984 - val_specificity: 0.9896 - val_miss_rate: 0.1928 - val_fall_out: 0.0104 - val_mcc: 0.8349
Epoch 21/100
63/63 [==============================] - 7s 101ms/step - loss: 0.4759 - accuracy: 0.8412 - recall: 0.7992 - precision: 0.8801 - AUROC: 0.9853 - AUPRC: 0.9175 - f1_score: 0.8377 - balanced_accuracy: 0.8936 - specificity: 0.9879 - miss_rate: 0.2008 - fall_out: 0.0121 - mcc: 0.8218 - val_loss: 0.4380 - val_accuracy: 0.8553 - val_recall: 0.8092 - val_precision: 0.8963 - val_AUROC: 0.9874 - val_AUPRC: 0.9345 - val_f1_score: 0.8505 - val_balanced_accuracy: 0.8994 - val_specificity: 0.9896 - val_miss_rate: 0.1908 - val_fall_out: 0.0104 - val_mcc: 0.8362
Epoch 22/100
63/63 [==============================] - 7s 100ms/step - loss: 0.4430 - accuracy: 0.8507 - recall: 0.8114 - precision: 0.8898 - AUROC: 0.9874 - AUPRC: 0.9278 - f1_score: 0.8488 - balanced_accuracy: 0.9001 - specificity: 0.9888 - miss_rate: 0.1886 - fall_out: 0.0112 - mcc: 0.8339 - val_loss: 0.4267 - val_accuracy: 0.8618 - val_recall: 0.8262 - val_precision: 0.8948 - val_AUROC: 0.9867 - val_AUPRC: 0.9378 - val_f1_score: 0.8592 - val_balanced_accuracy: 0.9077 - val_specificity: 0.9892 - val_miss_rate: 0.1738 - val_fall_out: 0.0108 - val_mcc: 0.8450
Epoch 23/100
63/63 [==============================] - 7s 101ms/step - loss: 0.3936 - accuracy: 0.8722 - recall: 0.8389 - precision: 0.9033 - AUROC: 0.9892 - AUPRC: 0.9413 - f1_score: 0.8699 - balanced_accuracy: 0.9145 - specificity: 0.9900 - miss_rate: 0.1611 - fall_out: 0.0100 - mcc: 0.8568 - val_loss: 0.4044 - val_accuracy: 0.8698 - val_recall: 0.8358 - val_precision: 0.9041 - val_AUROC: 0.9882 - val_AUPRC: 0.9424 - val_f1_score: 0.8686 - val_balanced_accuracy: 0.9130 - val_specificity: 0.9902 - val_miss_rate: 0.1642 - val_fall_out: 0.0098 - val_mcc: 0.8554
Epoch 24/100
63/63 [==============================] - 7s 105ms/step - loss: 0.3840 - accuracy: 0.8732 - recall: 0.8429 - precision: 0.9026 - AUROC: 0.9896 - AUPRC: 0.9420 - f1_score: 0.8718 - balanced_accuracy: 0.9164 - specificity: 0.9899 - miss_rate: 0.1571 - fall_out: 0.0101 - mcc: 0.8587 - val_loss: 0.3898 - val_accuracy: 0.8768 - val_recall: 0.8373 - val_precision: 0.9092 - val_AUROC: 0.9895 - val_AUPRC: 0.9460 - val_f1_score: 0.8717 - val_balanced_accuracy: 0.9140 - val_specificity: 0.9907 - val_miss_rate: 0.1627 - val_fall_out: 0.0093 - val_mcc: 0.8590
Epoch 25/100
63/63 [==============================] - 7s 102ms/step - loss: 0.3657 - accuracy: 0.8774 - recall: 0.8512 - precision: 0.9078 - AUROC: 0.9906 - AUPRC: 0.9449 - f1_score: 0.8786 - balanced_accuracy: 0.9208 - specificity: 0.9904 - miss_rate: 0.1488 - fall_out: 0.0096 - mcc: 0.8661 - val_loss: 0.3802 - val_accuracy: 0.8813 - val_recall: 0.8523 - val_precision: 0.9136 - val_AUROC: 0.9898 - val_AUPRC: 0.9492 - val_f1_score: 0.8819 - val_balanced_accuracy: 0.9217 - val_specificity: 0.9910 - val_miss_rate: 0.1477 - val_fall_out: 0.0090 - val_mcc: 0.8699
Epoch 26/100
63/63 [==============================] - 7s 102ms/step - loss: 0.3635 - accuracy: 0.8813 - recall: 0.8548 - precision: 0.9093 - AUROC: 0.9906 - AUPRC: 0.9467 - f1_score: 0.8812 - balanced_accuracy: 0.9227 - specificity: 0.9905 - miss_rate: 0.1452 - fall_out: 0.0095 - mcc: 0.8690 - val_loss: 0.3693 - val_accuracy: 0.8808 - val_recall: 0.8498 - val_precision: 0.9094 - val_AUROC: 0.9910 - val_AUPRC: 0.9521 - val_f1_score: 0.8786 - val_balanced_accuracy: 0.9202 - val_specificity: 0.9906 - val_miss_rate: 0.1502 - val_fall_out: 0.0094 - val_mcc: 0.8662
Epoch 27/100
63/63 [==============================] - 7s 102ms/step - loss: 0.3170 - accuracy: 0.8975 - recall: 0.8744 - precision: 0.9212 - AUROC: 0.9928 - AUPRC: 0.9586 - f1_score: 0.8972 - balanced_accuracy: 0.9330 - specificity: 0.9917 - miss_rate: 0.1256 - fall_out: 0.0083 - mcc: 0.8865 - val_loss: 0.3551 - val_accuracy: 0.8848 - val_recall: 0.8663 - val_precision: 0.9100 - val_AUROC: 0.9895 - val_AUPRC: 0.9516 - val_f1_score: 0.8876 - val_balanced_accuracy: 0.9284 - val_specificity: 0.9905 - val_miss_rate: 0.1337 - val_fall_out: 0.0095 - val_mcc: 0.8758
Epoch 28/100
63/63 [==============================] - 7s 103ms/step - loss: 0.3261 - accuracy: 0.8907 - recall: 0.8691 - precision: 0.9157 - AUROC: 0.9919 - AUPRC: 0.9554 - f1_score: 0.8918 - balanced_accuracy: 0.9301 - specificity: 0.9911 - miss_rate: 0.1309 - fall_out: 0.0089 - mcc: 0.8805 - val_loss: 0.3749 - val_accuracy: 0.8858 - val_recall: 0.8598 - val_precision: 0.9167 - val_AUROC: 0.9883 - val_AUPRC: 0.9497 - val_f1_score: 0.8873 - val_balanced_accuracy: 0.9256 - val_specificity: 0.9913 - val_miss_rate: 0.1402 - val_fall_out: 0.0087 - val_mcc: 0.8758
Epoch 29/100
63/63 [==============================] - 7s 102ms/step - loss: 0.2880 - accuracy: 0.9052 - recall: 0.8835 - precision: 0.9243 - AUROC: 0.9937 - AUPRC: 0.9639 - f1_score: 0.9034 - balanced_accuracy: 0.9377 - specificity: 0.9920 - miss_rate: 0.1165 - fall_out: 0.0080 - mcc: 0.8932 - val_loss: 0.3574 - val_accuracy: 0.8898 - val_recall: 0.8653 - val_precision: 0.9182 - val_AUROC: 0.9887 - val_AUPRC: 0.9512 - val_f1_score: 0.8910 - val_balanced_accuracy: 0.9284 - val_specificity: 0.9914 - val_miss_rate: 0.1347 - val_fall_out: 0.0086 - val_mcc: 0.8797
63/63 [==============================] - 4s 57ms/step - loss: 0.1212 - accuracy: 0.9652 - recall: 0.9542 - precision: 0.9758 - AUROC: 0.9992 - AUPRC: 0.9941 - f1_score: 0.9649 - balanced_accuracy: 0.9758 - specificity: 0.9974 - miss_rate: 0.0458 - fall_out: 0.0026 - mcc: 0.9611
16/16 [==============================] - 3s 162ms/step - loss: 0.3574 - accuracy: 0.8898 - recall: 0.8653 - precision: 0.9182 - AUROC: 0.9887 - AUPRC: 0.9512 - f1_score: 0.8910 - balanced_accuracy: 0.9284 - specificity: 0.9914 - miss_rate: 0.1347 - fall_out: 0.0086 - mcc: 0.8797
-- HOLDOUT 3 --
-- 6 Uncorrelated features: [Pearson+Spearman]
['mfcc10_var', 'mfcc17_mean', 'mfcc16_mean', 'mfcc11_var', 'tempo', 'mfcc19_mean']
-- Actually uncorrelated features: [confirmed via MIC]
[]
-- 1 Correlated features: [Pearson]
['spectral_centroid_mean']
- Removing uncorrelated/correlated features: ['spectral_centroid_mean']
- Building Fixed MLP:
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
features_data (InputLayer) [(None, 56)] 0
dense (Dense) (None, 256) 14592
dropout (Dropout) (None, 256) 0
dense_1 (Dense) (None, 256) 65792
dropout_1 (Dropout) (None, 256) 0
dense_2 (Dense) (None, 128) 32896
dropout_2 (Dropout) (None, 128) 0
dense_3 (Dense) (None, 128) 16512
dropout_3 (Dropout) (None, 128) 0
dense_4 (Dense) (None, 10) 1290
=================================================================
Total params: 131,082
Trainable params: 131,082
Non-trainable params: 0
_________________________________________________________________
- Building Fixed CNN:
Model: "model_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
mfccs_data (InputLayer) [(None, 20, 130, 1)] 0
conv2d (Conv2D) (None, 20, 130, 256) 2560
max_pooling2d (MaxPooling2D (None, 10, 65, 256) 0
)
dropout_4 (Dropout) (None, 10, 65, 256) 0
conv2d_1 (Conv2D) (None, 10, 65, 256) 590080
max_pooling2d_1 (MaxPooling (None, 5, 33, 256) 0
2D)
dropout_5 (Dropout) (None, 5, 33, 256) 0
conv2d_2 (Conv2D) (None, 5, 33, 256) 590080
max_pooling2d_2 (MaxPooling (None, 3, 17, 256) 0
2D)
dropout_6 (Dropout) (None, 3, 17, 256) 0
conv2d_3 (Conv2D) (None, 3, 17, 512) 2097664
max_pooling2d_3 (MaxPooling (None, 2, 9, 512) 0
2D)
flatten (Flatten) (None, 9216) 0
dense_5 (Dense) (None, 128) 1179776
dropout_7 (Dropout) (None, 128) 0
dense_6 (Dense) (None, 128) 16512
dropout_8 (Dropout) (None, 128) 0
dense_7 (Dense) (None, 10) 1290
=================================================================
Total params: 4,477,962
Trainable params: 4,477,962
Non-trainable params: 0
_________________________________________________________________
- Building MMNN:
Model: "model_2"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
mfccs_data (InputLayer) [(None, 20, 130, 1) 0 []
]
conv2d (Conv2D) (None, 20, 130, 256 2560 ['mfccs_data[0][0]']
)
max_pooling2d (MaxPooling2D) (None, 10, 65, 256) 0 ['conv2d[0][0]']
dropout_4 (Dropout) (None, 10, 65, 256) 0 ['max_pooling2d[0][0]']
conv2d_1 (Conv2D) (None, 10, 65, 256) 590080 ['dropout_4[0][0]']
max_pooling2d_1 (MaxPooling2D) (None, 5, 33, 256) 0 ['conv2d_1[0][0]']
dropout_5 (Dropout) (None, 5, 33, 256) 0 ['max_pooling2d_1[0][0]']
conv2d_2 (Conv2D) (None, 5, 33, 256) 590080 ['dropout_5[0][0]']
features_data (InputLayer) [(None, 56)] 0 []
max_pooling2d_2 (MaxPooling2D) (None, 3, 17, 256) 0 ['conv2d_2[0][0]']
dense (Dense) (None, 256) 14592 ['features_data[0][0]']
dropout_6 (Dropout) (None, 3, 17, 256) 0 ['max_pooling2d_2[0][0]']
dropout (Dropout) (None, 256) 0 ['dense[0][0]']
conv2d_3 (Conv2D) (None, 3, 17, 512) 2097664 ['dropout_6[0][0]']
dense_1 (Dense) (None, 256) 65792 ['dropout[0][0]']
max_pooling2d_3 (MaxPooling2D) (None, 2, 9, 512) 0 ['conv2d_3[0][0]']
dropout_1 (Dropout) (None, 256) 0 ['dense_1[0][0]']
flatten (Flatten) (None, 9216) 0 ['max_pooling2d_3[0][0]']
dense_2 (Dense) (None, 128) 32896 ['dropout_1[0][0]']
dense_5 (Dense) (None, 128) 1179776 ['flatten[0][0]']
dropout_2 (Dropout) (None, 128) 0 ['dense_2[0][0]']
dropout_7 (Dropout) (None, 128) 0 ['dense_5[0][0]']
dense_3 (Dense) (None, 128) 16512 ['dropout_2[0][0]']
dense_6 (Dense) (None, 128) 16512 ['dropout_7[0][0]']
dropout_3 (Dropout) (None, 128) 0 ['dense_3[0][0]']
dropout_8 (Dropout) (None, 128) 0 ['dense_6[0][0]']
concatenate (Concatenate) (None, 256) 0 ['dropout_3[0][0]',
'dropout_8[0][0]']
dense_8 (Dense) (None, 64) 16448 ['concatenate[0][0]']
dense_9 (Dense) (None, 10) 650 ['dense_8[0][0]']
==================================================================================================
Total params: 4,623,562
Trainable params: 4,623,562
Non-trainable params: 0
__________________________________________________________________________________________________
- Training MMNN model:
Epoch 1/100
63/63 [==============================] - 9s 108ms/step - loss: 2.1608 - accuracy: 0.2168 - recall: 0.0343 - precision: 0.5320 - AUROC: 0.6659 - AUPRC: 0.2087 - f1_score: 0.0645 - balanced_accuracy: 0.5155 - specificity: 0.9966 - miss_rate: 0.9657 - fall_out: 0.0034 - mcc: 0.1160 - val_loss: 1.6494 - val_accuracy: 0.3791 - val_recall: 0.1437 - val_precision: 0.7474 - val_AUROC: 0.8447 - val_AUPRC: 0.4386 - val_f1_score: 0.2411 - val_balanced_accuracy: 0.5692 - val_specificity: 0.9946 - val_miss_rate: 0.8563 - val_fall_out: 0.0054 - val_mcc: 0.3022
Epoch 2/100
63/63 [==============================] - 7s 104ms/step - loss: 1.6976 - accuracy: 0.3813 - recall: 0.1622 - precision: 0.6339 - AUROC: 0.8334 - AUPRC: 0.3958 - f1_score: 0.2583 - balanced_accuracy: 0.5759 - specificity: 0.9896 - miss_rate: 0.8378 - fall_out: 0.0104 - mcc: 0.2884 - val_loss: 1.4188 - val_accuracy: 0.4932 - val_recall: 0.2674 - val_precision: 0.7325 - val_AUROC: 0.8856 - val_AUPRC: 0.5366 - val_f1_score: 0.3918 - val_balanced_accuracy: 0.6283 - val_specificity: 0.9892 - val_miss_rate: 0.7326 - val_fall_out: 0.0108 - val_mcc: 0.4104
Epoch 3/100
63/63 [==============================] - 7s 103ms/step - loss: 1.4770 - accuracy: 0.4659 - recall: 0.2425 - precision: 0.6795 - AUROC: 0.8776 - AUPRC: 0.4998 - f1_score: 0.3574 - balanced_accuracy: 0.6149 - specificity: 0.9873 - miss_rate: 0.7575 - fall_out: 0.0127 - mcc: 0.3716 - val_loss: 1.2110 - val_accuracy: 0.5714 - val_recall: 0.3225 - val_precision: 0.7825 - val_AUROC: 0.9203 - val_AUPRC: 0.6339 - val_f1_score: 0.4567 - val_balanced_accuracy: 0.6563 - val_specificity: 0.9900 - val_miss_rate: 0.6775 - val_fall_out: 0.0100 - val_mcc: 0.4717
Epoch 4/100
63/63 [==============================] - 7s 105ms/step - loss: 1.3061 - accuracy: 0.5257 - recall: 0.3240 - precision: 0.7200 - AUROC: 0.9044 - AUPRC: 0.5813 - f1_score: 0.4469 - balanced_accuracy: 0.6550 - specificity: 0.9860 - miss_rate: 0.6760 - fall_out: 0.0140 - mcc: 0.4486 - val_loss: 1.0958 - val_accuracy: 0.6124 - val_recall: 0.4026 - val_precision: 0.8000 - val_AUROC: 0.9348 - val_AUPRC: 0.6892 - val_f1_score: 0.5356 - val_balanced_accuracy: 0.6957 - val_specificity: 0.9888 - val_miss_rate: 0.5974 - val_fall_out: 0.0112 - val_mcc: 0.5371
Epoch 5/100
63/63 [==============================] - 7s 103ms/step - loss: 1.2254 - accuracy: 0.5694 - recall: 0.3714 - precision: 0.7317 - AUROC: 0.9161 - AUPRC: 0.6205 - f1_score: 0.4927 - balanced_accuracy: 0.6781 - specificity: 0.9849 - miss_rate: 0.6286 - fall_out: 0.0151 - mcc: 0.4869 - val_loss: 1.0112 - val_accuracy: 0.6545 - val_recall: 0.4377 - val_precision: 0.8199 - val_AUROC: 0.9448 - val_AUPRC: 0.7273 - val_f1_score: 0.5707 - val_balanced_accuracy: 0.7135 - val_specificity: 0.9893 - val_miss_rate: 0.5623 - val_fall_out: 0.0107 - val_mcc: 0.5698
Epoch 6/100
63/63 [==============================] - 7s 104ms/step - loss: 1.1429 - accuracy: 0.6072 - recall: 0.4193 - precision: 0.7632 - AUROC: 0.9265 - AUPRC: 0.6629 - f1_score: 0.5413 - balanced_accuracy: 0.7024 - specificity: 0.9855 - miss_rate: 0.5807 - fall_out: 0.0145 - mcc: 0.5330 - val_loss: 0.9828 - val_accuracy: 0.6515 - val_recall: 0.4812 - val_precision: 0.7819 - val_AUROC: 0.9470 - val_AUPRC: 0.7340 - val_f1_score: 0.5958 - val_balanced_accuracy: 0.7332 - val_specificity: 0.9851 - val_miss_rate: 0.5188 - val_fall_out: 0.0149 - val_mcc: 0.5821
Epoch 7/100
63/63 [==============================] - 7s 102ms/step - loss: 1.0672 - accuracy: 0.6321 - recall: 0.4728 - precision: 0.7665 - AUROC: 0.9360 - AUPRC: 0.6983 - f1_score: 0.5849 - balanced_accuracy: 0.7284 - specificity: 0.9840 - miss_rate: 0.5272 - fall_out: 0.0160 - mcc: 0.5696 - val_loss: 0.9728 - val_accuracy: 0.6575 - val_recall: 0.4942 - val_precision: 0.8037 - val_AUROC: 0.9493 - val_AUPRC: 0.7464 - val_f1_score: 0.6121 - val_balanced_accuracy: 0.7404 - val_specificity: 0.9866 - val_miss_rate: 0.5058 - val_fall_out: 0.0134 - val_mcc: 0.6005
Epoch 8/100
63/63 [==============================] - 7s 103ms/step - loss: 1.0056 - accuracy: 0.6599 - recall: 0.5124 - precision: 0.7797 - AUROC: 0.9425 - AUPRC: 0.7252 - f1_score: 0.6184 - balanced_accuracy: 0.7482 - specificity: 0.9839 - miss_rate: 0.4876 - fall_out: 0.0161 - mcc: 0.6009 - val_loss: 0.8303 - val_accuracy: 0.7196 - val_recall: 0.5769 - val_precision: 0.8252 - val_AUROC: 0.9618 - val_AUPRC: 0.7988 - val_f1_score: 0.6790 - val_balanced_accuracy: 0.7816 - val_specificity: 0.9864 - val_miss_rate: 0.4231 - val_fall_out: 0.0136 - val_mcc: 0.6627
Epoch 9/100
63/63 [==============================] - 7s 104ms/step - loss: 0.9475 - accuracy: 0.6819 - recall: 0.5456 - precision: 0.7890 - AUROC: 0.9488 - AUPRC: 0.7474 - f1_score: 0.6451 - balanced_accuracy: 0.7647 - specificity: 0.9838 - miss_rate: 0.4544 - fall_out: 0.0162 - mcc: 0.6260 - val_loss: 0.7654 - val_accuracy: 0.7481 - val_recall: 0.6204 - val_precision: 0.8452 - val_AUROC: 0.9673 - val_AUPRC: 0.8265 - val_f1_score: 0.7156 - val_balanced_accuracy: 0.8039 - val_specificity: 0.9874 - val_miss_rate: 0.3796 - val_fall_out: 0.0126 - val_mcc: 0.6991
Epoch 10/100
63/63 [==============================] - 7s 103ms/step - loss: 0.8877 - accuracy: 0.7054 - recall: 0.5952 - precision: 0.7968 - AUROC: 0.9544 - AUPRC: 0.7747 - f1_score: 0.6814 - balanced_accuracy: 0.7892 - specificity: 0.9831 - miss_rate: 0.4048 - fall_out: 0.0169 - mcc: 0.6599 - val_loss: 0.7470 - val_accuracy: 0.7581 - val_recall: 0.6299 - val_precision: 0.8460 - val_AUROC: 0.9684 - val_AUPRC: 0.8345 - val_f1_score: 0.7222 - val_balanced_accuracy: 0.8086 - val_specificity: 0.9873 - val_miss_rate: 0.3701 - val_fall_out: 0.0127 - val_mcc: 0.7053
Epoch 11/100
63/63 [==============================] - 7s 103ms/step - loss: 0.8337 - accuracy: 0.7209 - recall: 0.6195 - precision: 0.8118 - AUROC: 0.9592 - AUPRC: 0.7958 - f1_score: 0.7027 - balanced_accuracy: 0.8018 - specificity: 0.9840 - miss_rate: 0.3805 - fall_out: 0.0160 - mcc: 0.6819 - val_loss: 0.7026 - val_accuracy: 0.7636 - val_recall: 0.6620 - val_precision: 0.8535 - val_AUROC: 0.9724 - val_AUPRC: 0.8515 - val_f1_score: 0.7456 - val_balanced_accuracy: 0.8247 - val_specificity: 0.9874 - val_miss_rate: 0.3380 - val_fall_out: 0.0126 - val_mcc: 0.7283
Epoch 12/100
63/63 [==============================] - 7s 104ms/step - loss: 0.8083 - accuracy: 0.7303 - recall: 0.6351 - precision: 0.8158 - AUROC: 0.9617 - AUPRC: 0.8098 - f1_score: 0.7142 - balanced_accuracy: 0.8096 - specificity: 0.9841 - miss_rate: 0.3649 - fall_out: 0.0159 - mcc: 0.6933 - val_loss: 0.6791 - val_accuracy: 0.7727 - val_recall: 0.6635 - val_precision: 0.8604 - val_AUROC: 0.9744 - val_AUPRC: 0.8596 - val_f1_score: 0.7492 - val_balanced_accuracy: 0.8258 - val_specificity: 0.9880 - val_miss_rate: 0.3365 - val_fall_out: 0.0120 - val_mcc: 0.7327
Epoch 13/100
63/63 [==============================] - 7s 103ms/step - loss: 0.7750 - accuracy: 0.7401 - recall: 0.6440 - precision: 0.8221 - AUROC: 0.9648 - AUPRC: 0.8184 - f1_score: 0.7222 - balanced_accuracy: 0.8143 - specificity: 0.9845 - miss_rate: 0.3560 - fall_out: 0.0155 - mcc: 0.7017 - val_loss: 0.6451 - val_accuracy: 0.7882 - val_recall: 0.7121 - val_precision: 0.8556 - val_AUROC: 0.9755 - val_AUPRC: 0.8707 - val_f1_score: 0.7773 - val_balanced_accuracy: 0.8494 - val_specificity: 0.9866 - val_miss_rate: 0.2879 - val_fall_out: 0.0134 - val_mcc: 0.7589
Epoch 14/100
63/63 [==============================] - 7s 104ms/step - loss: 0.7177 - accuracy: 0.7596 - recall: 0.6829 - precision: 0.8331 - AUROC: 0.9690 - AUPRC: 0.8409 - f1_score: 0.7506 - balanced_accuracy: 0.8338 - specificity: 0.9848 - miss_rate: 0.3171 - fall_out: 0.0152 - mcc: 0.7302 - val_loss: 0.6332 - val_accuracy: 0.7877 - val_recall: 0.7036 - val_precision: 0.8689 - val_AUROC: 0.9770 - val_AUPRC: 0.8743 - val_f1_score: 0.7775 - val_balanced_accuracy: 0.8459 - val_specificity: 0.9882 - val_miss_rate: 0.2964 - val_fall_out: 0.0118 - val_mcc: 0.7608
Epoch 15/100
63/63 [==============================] - 7s 103ms/step - loss: 0.7009 - accuracy: 0.7663 - recall: 0.6936 - precision: 0.8368 - AUROC: 0.9701 - AUPRC: 0.8447 - f1_score: 0.7585 - balanced_accuracy: 0.8393 - specificity: 0.9850 - miss_rate: 0.3064 - fall_out: 0.0150 - mcc: 0.7384 - val_loss: 0.5899 - val_accuracy: 0.8067 - val_recall: 0.7331 - val_precision: 0.8725 - val_AUROC: 0.9797 - val_AUPRC: 0.8883 - val_f1_score: 0.7967 - val_balanced_accuracy: 0.8606 - val_specificity: 0.9881 - val_miss_rate: 0.2669 - val_fall_out: 0.0119 - val_mcc: 0.7799
Epoch 16/100
63/63 [==============================] - 7s 102ms/step - loss: 0.6661 - accuracy: 0.7782 - recall: 0.7073 - precision: 0.8442 - AUROC: 0.9730 - AUPRC: 0.8580 - f1_score: 0.7697 - balanced_accuracy: 0.8464 - specificity: 0.9855 - miss_rate: 0.2927 - fall_out: 0.0145 - mcc: 0.7502 - val_loss: 0.5652 - val_accuracy: 0.8152 - val_recall: 0.7556 - val_precision: 0.8697 - val_AUROC: 0.9808 - val_AUPRC: 0.8932 - val_f1_score: 0.8087 - val_balanced_accuracy: 0.8715 - val_specificity: 0.9874 - val_miss_rate: 0.2444 - val_fall_out: 0.0126 - val_mcc: 0.7914
Epoch 17/100
63/63 [==============================] - 7s 103ms/step - loss: 0.6555 - accuracy: 0.7798 - recall: 0.7123 - precision: 0.8478 - AUROC: 0.9737 - AUPRC: 0.8629 - f1_score: 0.7742 - balanced_accuracy: 0.8490 - specificity: 0.9858 - miss_rate: 0.2877 - fall_out: 0.0142 - mcc: 0.7549 - val_loss: 0.5679 - val_accuracy: 0.8032 - val_recall: 0.7481 - val_precision: 0.8711 - val_AUROC: 0.9811 - val_AUPRC: 0.8920 - val_f1_score: 0.8050 - val_balanced_accuracy: 0.8679 - val_specificity: 0.9877 - val_miss_rate: 0.2519 - val_fall_out: 0.0123 - val_mcc: 0.7879
Epoch 18/100
63/63 [==============================] - 7s 104ms/step - loss: 0.6080 - accuracy: 0.7977 - recall: 0.7384 - precision: 0.8561 - AUROC: 0.9769 - AUPRC: 0.8779 - f1_score: 0.7929 - balanced_accuracy: 0.8623 - specificity: 0.9862 - miss_rate: 0.2616 - fall_out: 0.0138 - mcc: 0.7743 - val_loss: 0.5333 - val_accuracy: 0.8152 - val_recall: 0.7576 - val_precision: 0.8761 - val_AUROC: 0.9830 - val_AUPRC: 0.9049 - val_f1_score: 0.8126 - val_balanced_accuracy: 0.8729 - val_specificity: 0.9881 - val_miss_rate: 0.2424 - val_fall_out: 0.0119 - val_mcc: 0.7960
Epoch 19/100
63/63 [==============================] - 7s 105ms/step - loss: 0.5784 - accuracy: 0.8092 - recall: 0.7543 - precision: 0.8664 - AUROC: 0.9790 - AUPRC: 0.8892 - f1_score: 0.8064 - balanced_accuracy: 0.8707 - specificity: 0.9871 - miss_rate: 0.2457 - fall_out: 0.0129 - mcc: 0.7889 - val_loss: 0.5125 - val_accuracy: 0.8247 - val_recall: 0.7832 - val_precision: 0.8787 - val_AUROC: 0.9837 - val_AUPRC: 0.9090 - val_f1_score: 0.8282 - val_balanced_accuracy: 0.8856 - val_specificity: 0.9880 - val_miss_rate: 0.2168 - val_fall_out: 0.0120 - val_mcc: 0.8119
Epoch 20/100
63/63 [==============================] - 7s 103ms/step - loss: 0.5566 - accuracy: 0.8183 - recall: 0.7648 - precision: 0.8694 - AUROC: 0.9802 - AUPRC: 0.8951 - f1_score: 0.8138 - balanced_accuracy: 0.8760 - specificity: 0.9872 - miss_rate: 0.2352 - fall_out: 0.0128 - mcc: 0.7965 - val_loss: 0.4959 - val_accuracy: 0.8368 - val_recall: 0.7917 - val_precision: 0.8877 - val_AUROC: 0.9845 - val_AUPRC: 0.9134 - val_f1_score: 0.8370 - val_balanced_accuracy: 0.8903 - val_specificity: 0.9889 - val_miss_rate: 0.2083 - val_fall_out: 0.0111 - val_mcc: 0.8216
Epoch 21/100
63/63 [==============================] - 7s 103ms/step - loss: 0.5126 - accuracy: 0.8278 - recall: 0.7844 - precision: 0.8751 - AUROC: 0.9834 - AUPRC: 0.9074 - f1_score: 0.8273 - balanced_accuracy: 0.8860 - specificity: 0.9876 - miss_rate: 0.2156 - fall_out: 0.0124 - mcc: 0.8107 - val_loss: 0.4624 - val_accuracy: 0.8383 - val_recall: 0.8042 - val_precision: 0.8795 - val_AUROC: 0.9864 - val_AUPRC: 0.9249 - val_f1_score: 0.8402 - val_balanced_accuracy: 0.8960 - val_specificity: 0.9878 - val_miss_rate: 0.1958 - val_fall_out: 0.0122 - val_mcc: 0.8243
Epoch 22/100
63/63 [==============================] - 7s 104ms/step - loss: 0.5033 - accuracy: 0.8313 - recall: 0.7876 - precision: 0.8716 - AUROC: 0.9836 - AUPRC: 0.9105 - f1_score: 0.8275 - balanced_accuracy: 0.8873 - specificity: 0.9871 - miss_rate: 0.2124 - fall_out: 0.0129 - mcc: 0.8107 - val_loss: 0.4875 - val_accuracy: 0.8358 - val_recall: 0.7902 - val_precision: 0.8796 - val_AUROC: 0.9846 - val_AUPRC: 0.9154 - val_f1_score: 0.8325 - val_balanced_accuracy: 0.8891 - val_specificity: 0.9880 - val_miss_rate: 0.2098 - val_fall_out: 0.0120 - val_mcc: 0.8164
Epoch 23/100
63/63 [==============================] - 7s 103ms/step - loss: 0.4926 - accuracy: 0.8406 - recall: 0.7971 - precision: 0.8801 - AUROC: 0.9841 - AUPRC: 0.9141 - f1_score: 0.8365 - balanced_accuracy: 0.8925 - specificity: 0.9879 - miss_rate: 0.2029 - fall_out: 0.0121 - mcc: 0.8206 - val_loss: 0.4339 - val_accuracy: 0.8583 - val_recall: 0.8242 - val_precision: 0.8917 - val_AUROC: 0.9878 - val_AUPRC: 0.9312 - val_f1_score: 0.8566 - val_balanced_accuracy: 0.9066 - val_specificity: 0.9889 - val_miss_rate: 0.1758 - val_fall_out: 0.0111 - val_mcc: 0.8422
Epoch 24/100
63/63 [==============================] - 7s 104ms/step - loss: 0.4637 - accuracy: 0.8456 - recall: 0.8051 - precision: 0.8839 - AUROC: 0.9861 - AUPRC: 0.9224 - f1_score: 0.8427 - balanced_accuracy: 0.8967 - specificity: 0.9883 - miss_rate: 0.1949 - fall_out: 0.0117 - mcc: 0.8272 - val_loss: 0.4112 - val_accuracy: 0.8598 - val_recall: 0.8222 - val_precision: 0.8973 - val_AUROC: 0.9897 - val_AUPRC: 0.9382 - val_f1_score: 0.8581 - val_balanced_accuracy: 0.9059 - val_specificity: 0.9895 - val_miss_rate: 0.1778 - val_fall_out: 0.0105 - val_mcc: 0.8441
Epoch 25/100
63/63 [==============================] - 7s 103ms/step - loss: 0.4234 - accuracy: 0.8598 - recall: 0.8274 - precision: 0.8985 - AUROC: 0.9877 - AUPRC: 0.9328 - f1_score: 0.8615 - balanced_accuracy: 0.9085 - specificity: 0.9896 - miss_rate: 0.1726 - fall_out: 0.0104 - mcc: 0.8477 - val_loss: 0.4198 - val_accuracy: 0.8613 - val_recall: 0.8312 - val_precision: 0.8901 - val_AUROC: 0.9887 - val_AUPRC: 0.9345 - val_f1_score: 0.8597 - val_balanced_accuracy: 0.9099 - val_specificity: 0.9886 - val_miss_rate: 0.1688 - val_fall_out: 0.0114 - val_mcc: 0.8453
Epoch 26/100
63/63 [==============================] - 7s 104ms/step - loss: 0.4327 - accuracy: 0.8558 - recall: 0.8198 - precision: 0.8889 - AUROC: 0.9871 - AUPRC: 0.9294 - f1_score: 0.8529 - balanced_accuracy: 0.9042 - specificity: 0.9886 - miss_rate: 0.1802 - fall_out: 0.0114 - mcc: 0.8382 - val_loss: 0.4289 - val_accuracy: 0.8588 - val_recall: 0.8358 - val_precision: 0.8906 - val_AUROC: 0.9876 - val_AUPRC: 0.9325 - val_f1_score: 0.8623 - val_balanced_accuracy: 0.9122 - val_specificity: 0.9886 - val_miss_rate: 0.1642 - val_fall_out: 0.0114 - val_mcc: 0.8481
63/63 [==============================] - 4s 59ms/step - loss: 0.2043 - accuracy: 0.9367 - recall: 0.9158 - precision: 0.9568 - AUROC: 0.9978 - AUPRC: 0.9839 - f1_score: 0.9359 - balanced_accuracy: 0.9556 - specificity: 0.9954 - miss_rate: 0.0842 - fall_out: 0.0046 - mcc: 0.9292
16/16 [==============================] - 3s 167ms/step - loss: 0.4289 - accuracy: 0.8588 - recall: 0.8358 - precision: 0.8906 - AUROC: 0.9876 - AUPRC: 0.9325 - f1_score: 0.8623 - balanced_accuracy: 0.9122 - specificity: 0.9886 - miss_rate: 0.1642 - fall_out: 0.0114 - mcc: 0.8481
-- HOLDOUT 4 --
-- 5 Uncorrelated features: [Pearson+Spearman]
['mfcc10_var', 'mfcc17_mean', 'mfcc16_mean', 'tempo', 'mfcc19_mean']
-- Actually uncorrelated features: [confirmed via MIC]
[]
-- 1 Correlated features: [Pearson]
['spectral_centroid_mean']
- Removing uncorrelated/correlated features: ['spectral_centroid_mean']
- Building Fixed MLP:
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
features_data (InputLayer) [(None, 56)] 0
dense (Dense) (None, 256) 14592
dropout (Dropout) (None, 256) 0
dense_1 (Dense) (None, 256) 65792
dropout_1 (Dropout) (None, 256) 0
dense_2 (Dense) (None, 128) 32896
dropout_2 (Dropout) (None, 128) 0
dense_3 (Dense) (None, 128) 16512
dropout_3 (Dropout) (None, 128) 0
dense_4 (Dense) (None, 10) 1290
=================================================================
Total params: 131,082
Trainable params: 131,082
Non-trainable params: 0
_________________________________________________________________
- Building Fixed CNN:
Model: "model_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
mfccs_data (InputLayer) [(None, 20, 130, 1)] 0
conv2d (Conv2D) (None, 20, 130, 256) 2560
max_pooling2d (MaxPooling2D (None, 10, 65, 256) 0
)
dropout_4 (Dropout) (None, 10, 65, 256) 0
conv2d_1 (Conv2D) (None, 10, 65, 256) 590080
max_pooling2d_1 (MaxPooling (None, 5, 33, 256) 0
2D)
dropout_5 (Dropout) (None, 5, 33, 256) 0
conv2d_2 (Conv2D) (None, 5, 33, 256) 590080
max_pooling2d_2 (MaxPooling (None, 3, 17, 256) 0
2D)
dropout_6 (Dropout) (None, 3, 17, 256) 0
conv2d_3 (Conv2D) (None, 3, 17, 512) 2097664
max_pooling2d_3 (MaxPooling (None, 2, 9, 512) 0
2D)
flatten (Flatten) (None, 9216) 0
dense_5 (Dense) (None, 128) 1179776
dropout_7 (Dropout) (None, 128) 0
dense_6 (Dense) (None, 128) 16512
dropout_8 (Dropout) (None, 128) 0
dense_7 (Dense) (None, 10) 1290
=================================================================
Total params: 4,477,962
Trainable params: 4,477,962
Non-trainable params: 0
_________________________________________________________________
- Building MMNN:
Model: "model_2"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
mfccs_data (InputLayer) [(None, 20, 130, 1) 0 []
]
conv2d (Conv2D) (None, 20, 130, 256 2560 ['mfccs_data[0][0]']
)
max_pooling2d (MaxPooling2D) (None, 10, 65, 256) 0 ['conv2d[0][0]']
dropout_4 (Dropout) (None, 10, 65, 256) 0 ['max_pooling2d[0][0]']
conv2d_1 (Conv2D) (None, 10, 65, 256) 590080 ['dropout_4[0][0]']
max_pooling2d_1 (MaxPooling2D) (None, 5, 33, 256) 0 ['conv2d_1[0][0]']
dropout_5 (Dropout) (None, 5, 33, 256) 0 ['max_pooling2d_1[0][0]']
conv2d_2 (Conv2D) (None, 5, 33, 256) 590080 ['dropout_5[0][0]']
features_data (InputLayer) [(None, 56)] 0 []
max_pooling2d_2 (MaxPooling2D) (None, 3, 17, 256) 0 ['conv2d_2[0][0]']
dense (Dense) (None, 256) 14592 ['features_data[0][0]']
dropout_6 (Dropout) (None, 3, 17, 256) 0 ['max_pooling2d_2[0][0]']
dropout (Dropout) (None, 256) 0 ['dense[0][0]']
conv2d_3 (Conv2D) (None, 3, 17, 512) 2097664 ['dropout_6[0][0]']
dense_1 (Dense) (None, 256) 65792 ['dropout[0][0]']
max_pooling2d_3 (MaxPooling2D) (None, 2, 9, 512) 0 ['conv2d_3[0][0]']
dropout_1 (Dropout) (None, 256) 0 ['dense_1[0][0]']
flatten (Flatten) (None, 9216) 0 ['max_pooling2d_3[0][0]']
dense_2 (Dense) (None, 128) 32896 ['dropout_1[0][0]']
dense_5 (Dense) (None, 128) 1179776 ['flatten[0][0]']
dropout_2 (Dropout) (None, 128) 0 ['dense_2[0][0]']
dropout_7 (Dropout) (None, 128) 0 ['dense_5[0][0]']
dense_3 (Dense) (None, 128) 16512 ['dropout_2[0][0]']
dense_6 (Dense) (None, 128) 16512 ['dropout_7[0][0]']
dropout_3 (Dropout) (None, 128) 0 ['dense_3[0][0]']
dropout_8 (Dropout) (None, 128) 0 ['dense_6[0][0]']
concatenate (Concatenate) (None, 256) 0 ['dropout_3[0][0]',
'dropout_8[0][0]']
dense_8 (Dense) (None, 64) 16448 ['concatenate[0][0]']
dense_9 (Dense) (None, 10) 650 ['dense_8[0][0]']
==================================================================================================
Total params: 4,623,562
Trainable params: 4,623,562
Non-trainable params: 0
__________________________________________________________________________________________________
- Training MMNN model:
Epoch 1/100
63/63 [==============================] - 9s 110ms/step - loss: 2.1879 - accuracy: 0.2082 - recall: 0.0237 - precision: 0.4532 - AUROC: 0.6595 - AUPRC: 0.1915 - f1_score: 0.0450 - balanced_accuracy: 0.5102 - specificity: 0.9968 - miss_rate: 0.9763 - fall_out: 0.0032 - mcc: 0.0853 - val_loss: 1.6617 - val_accuracy: 0.4226 - val_recall: 0.0896 - val_precision: 0.8174 - val_AUROC: 0.8570 - val_AUPRC: 0.4364 - val_f1_score: 0.1616 - val_balanced_accuracy: 0.5437 - val_specificity: 0.9978 - val_miss_rate: 0.9104 - val_fall_out: 0.0022 - val_mcc: 0.2518
Epoch 2/100
63/63 [==============================] - 7s 104ms/step - loss: 1.6782 - accuracy: 0.3846 - recall: 0.1542 - precision: 0.6158 - AUROC: 0.8369 - AUPRC: 0.3956 - f1_score: 0.2466 - balanced_accuracy: 0.5717 - specificity: 0.9893 - miss_rate: 0.8458 - fall_out: 0.0107 - mcc: 0.2755 - val_loss: 1.3251 - val_accuracy: 0.5023 - val_recall: 0.2554 - val_precision: 0.7658 - val_AUROC: 0.9063 - val_AUPRC: 0.5814 - val_f1_score: 0.3830 - val_balanced_accuracy: 0.6234 - val_specificity: 0.9913 - val_miss_rate: 0.7446 - val_fall_out: 0.0087 - val_mcc: 0.4122
Epoch 3/100
63/63 [==============================] - 7s 105ms/step - loss: 1.4563 - accuracy: 0.4724 - recall: 0.2454 - precision: 0.6672 - AUROC: 0.8811 - AUPRC: 0.5025 - f1_score: 0.3588 - balanced_accuracy: 0.6159 - specificity: 0.9864 - miss_rate: 0.7546 - fall_out: 0.0136 - mcc: 0.3694 - val_loss: 1.1561 - val_accuracy: 0.5784 - val_recall: 0.3465 - val_precision: 0.8238 - val_AUROC: 0.9292 - val_AUPRC: 0.6658 - val_f1_score: 0.4878 - val_balanced_accuracy: 0.6691 - val_specificity: 0.9918 - val_miss_rate: 0.6535 - val_fall_out: 0.0082 - val_mcc: 0.5056
Epoch 4/100
63/63 [==============================] - 7s 104ms/step - loss: 1.2967 - accuracy: 0.5334 - recall: 0.3302 - precision: 0.7073 - AUROC: 0.9063 - AUPRC: 0.5807 - f1_score: 0.4502 - balanced_accuracy: 0.6575 - specificity: 0.9848 - miss_rate: 0.6698 - fall_out: 0.0152 - mcc: 0.4479 - val_loss: 1.0432 - val_accuracy: 0.6214 - val_recall: 0.4126 - val_precision: 0.8039 - val_AUROC: 0.9430 - val_AUPRC: 0.7061 - val_f1_score: 0.5453 - val_balanced_accuracy: 0.7007 - val_specificity: 0.9888 - val_miss_rate: 0.5874 - val_fall_out: 0.0112 - val_mcc: 0.5458
Epoch 5/100
63/63 [==============================] - 7s 104ms/step - loss: 1.2114 - accuracy: 0.5775 - recall: 0.3806 - precision: 0.7302 - AUROC: 0.9178 - AUPRC: 0.6246 - f1_score: 0.5004 - balanced_accuracy: 0.6825 - specificity: 0.9844 - miss_rate: 0.6194 - fall_out: 0.0156 - mcc: 0.4926 - val_loss: 0.9415 - val_accuracy: 0.6660 - val_recall: 0.4652 - val_precision: 0.8287 - val_AUROC: 0.9541 - val_AUPRC: 0.7589 - val_f1_score: 0.5959 - val_balanced_accuracy: 0.7273 - val_specificity: 0.9893 - val_miss_rate: 0.5348 - val_fall_out: 0.0107 - val_mcc: 0.5924
Epoch 6/100
63/63 [==============================] - 7s 104ms/step - loss: 1.1073 - accuracy: 0.6147 - recall: 0.4389 - precision: 0.7619 - AUROC: 0.9318 - AUPRC: 0.6764 - f1_score: 0.5569 - balanced_accuracy: 0.7118 - specificity: 0.9848 - miss_rate: 0.5611 - fall_out: 0.0152 - mcc: 0.5455 - val_loss: 0.8479 - val_accuracy: 0.7136 - val_recall: 0.5563 - val_precision: 0.8474 - val_AUROC: 0.9611 - val_AUPRC: 0.8006 - val_f1_score: 0.6717 - val_balanced_accuracy: 0.7726 - val_specificity: 0.9889 - val_miss_rate: 0.4437 - val_fall_out: 0.0111 - val_mcc: 0.6604
Epoch 7/100
63/63 [==============================] - 7s 104ms/step - loss: 1.0322 - accuracy: 0.6413 - recall: 0.4899 - precision: 0.7728 - AUROC: 0.9397 - AUPRC: 0.7091 - f1_score: 0.5996 - balanced_accuracy: 0.7369 - specificity: 0.9840 - miss_rate: 0.5101 - fall_out: 0.0160 - mcc: 0.5834 - val_loss: 0.8123 - val_accuracy: 0.7141 - val_recall: 0.5819 - val_precision: 0.8396 - val_AUROC: 0.9636 - val_AUPRC: 0.8073 - val_f1_score: 0.6874 - val_balanced_accuracy: 0.7848 - val_specificity: 0.9876 - val_miss_rate: 0.4181 - val_fall_out: 0.0124 - val_mcc: 0.6727
Epoch 8/100
63/63 [==============================] - 7s 105ms/step - loss: 0.9730 - accuracy: 0.6573 - recall: 0.5243 - precision: 0.7832 - AUROC: 0.9462 - AUPRC: 0.7360 - f1_score: 0.6281 - balanced_accuracy: 0.7541 - specificity: 0.9839 - miss_rate: 0.4757 - fall_out: 0.0161 - mcc: 0.6100 - val_loss: 0.7556 - val_accuracy: 0.7341 - val_recall: 0.6350 - val_precision: 0.8326 - val_AUROC: 0.9680 - val_AUPRC: 0.8276 - val_f1_score: 0.7205 - val_balanced_accuracy: 0.8104 - val_specificity: 0.9858 - val_miss_rate: 0.3650 - val_fall_out: 0.0142 - val_mcc: 0.7016
Epoch 9/100
63/63 [==============================] - 7s 104ms/step - loss: 0.8960 - accuracy: 0.6921 - recall: 0.5683 - precision: 0.8016 - AUROC: 0.9540 - AUPRC: 0.7699 - f1_score: 0.6651 - balanced_accuracy: 0.7763 - specificity: 0.9844 - miss_rate: 0.4317 - fall_out: 0.0156 - mcc: 0.6460 - val_loss: 0.7219 - val_accuracy: 0.7466 - val_recall: 0.6535 - val_precision: 0.8419 - val_AUROC: 0.9705 - val_AUPRC: 0.8419 - val_f1_score: 0.7358 - val_balanced_accuracy: 0.8199 - val_specificity: 0.9864 - val_miss_rate: 0.3465 - val_fall_out: 0.0136 - val_mcc: 0.7174
Epoch 10/100
63/63 [==============================] - 7s 106ms/step - loss: 0.8639 - accuracy: 0.7050 - recall: 0.5911 - precision: 0.8036 - AUROC: 0.9567 - AUPRC: 0.7820 - f1_score: 0.6811 - balanced_accuracy: 0.7875 - specificity: 0.9840 - miss_rate: 0.4089 - fall_out: 0.0160 - mcc: 0.6609 - val_loss: 0.6814 - val_accuracy: 0.7737 - val_recall: 0.6740 - val_precision: 0.8590 - val_AUROC: 0.9733 - val_AUPRC: 0.8570 - val_f1_score: 0.7553 - val_balanced_accuracy: 0.8309 - val_specificity: 0.9877 - val_miss_rate: 0.3260 - val_fall_out: 0.0123 - val_mcc: 0.7382
Epoch 11/100
63/63 [==============================] - 7s 104ms/step - loss: 0.8179 - accuracy: 0.7181 - recall: 0.6159 - precision: 0.8104 - AUROC: 0.9611 - AUPRC: 0.8024 - f1_score: 0.6999 - balanced_accuracy: 0.7999 - specificity: 0.9840 - miss_rate: 0.3841 - fall_out: 0.0160 - mcc: 0.6791 - val_loss: 0.6332 - val_accuracy: 0.7862 - val_recall: 0.6995 - val_precision: 0.8656 - val_AUROC: 0.9770 - val_AUPRC: 0.8738 - val_f1_score: 0.7737 - val_balanced_accuracy: 0.8437 - val_specificity: 0.9879 - val_miss_rate: 0.3005 - val_fall_out: 0.0121 - val_mcc: 0.7567
Epoch 12/100
63/63 [==============================] - 7s 104ms/step - loss: 0.7772 - accuracy: 0.7368 - recall: 0.6404 - precision: 0.8195 - AUROC: 0.9646 - AUPRC: 0.8177 - f1_score: 0.7190 - balanced_accuracy: 0.8124 - specificity: 0.9843 - miss_rate: 0.3596 - fall_out: 0.0157 - mcc: 0.6983 - val_loss: 0.6336 - val_accuracy: 0.7867 - val_recall: 0.7001 - val_precision: 0.8651 - val_AUROC: 0.9770 - val_AUPRC: 0.8746 - val_f1_score: 0.7739 - val_balanced_accuracy: 0.8440 - val_specificity: 0.9879 - val_miss_rate: 0.2999 - val_fall_out: 0.0121 - val_mcc: 0.7567
Epoch 13/100
63/63 [==============================] - 7s 104ms/step - loss: 0.7423 - accuracy: 0.7461 - recall: 0.6578 - precision: 0.8246 - AUROC: 0.9674 - AUPRC: 0.8309 - f1_score: 0.7318 - balanced_accuracy: 0.8211 - specificity: 0.9845 - miss_rate: 0.3422 - fall_out: 0.0155 - mcc: 0.7112 - val_loss: 0.5765 - val_accuracy: 0.8102 - val_recall: 0.7331 - val_precision: 0.8709 - val_AUROC: 0.9803 - val_AUPRC: 0.8914 - val_f1_score: 0.7961 - val_balanced_accuracy: 0.8605 - val_specificity: 0.9879 - val_miss_rate: 0.2669 - val_fall_out: 0.0121 - val_mcc: 0.7791
Epoch 14/100
63/63 [==============================] - 7s 104ms/step - loss: 0.6816 - accuracy: 0.7712 - recall: 0.6921 - precision: 0.8428 - AUROC: 0.9723 - AUPRC: 0.8521 - f1_score: 0.7601 - balanced_accuracy: 0.8389 - specificity: 0.9857 - miss_rate: 0.3079 - fall_out: 0.0143 - mcc: 0.7406 - val_loss: 0.5647 - val_accuracy: 0.8032 - val_recall: 0.7391 - val_precision: 0.8775 - val_AUROC: 0.9817 - val_AUPRC: 0.8970 - val_f1_score: 0.8024 - val_balanced_accuracy: 0.8638 - val_specificity: 0.9885 - val_miss_rate: 0.2609 - val_fall_out: 0.0115 - val_mcc: 0.7860
Epoch 15/100
63/63 [==============================] - 7s 104ms/step - loss: 0.6628 - accuracy: 0.7827 - recall: 0.7122 - precision: 0.8478 - AUROC: 0.9731 - AUPRC: 0.8593 - f1_score: 0.7741 - balanced_accuracy: 0.8490 - specificity: 0.9858 - miss_rate: 0.2878 - fall_out: 0.0142 - mcc: 0.7548 - val_loss: 0.5187 - val_accuracy: 0.8282 - val_recall: 0.7586 - val_precision: 0.8870 - val_AUROC: 0.9840 - val_AUPRC: 0.9124 - val_f1_score: 0.8178 - val_balanced_accuracy: 0.8739 - val_specificity: 0.9893 - val_miss_rate: 0.2414 - val_fall_out: 0.0107 - val_mcc: 0.8023
Epoch 16/100
63/63 [==============================] - 7s 104ms/step - loss: 0.6196 - accuracy: 0.7947 - recall: 0.7271 - precision: 0.8553 - AUROC: 0.9766 - AUPRC: 0.8741 - f1_score: 0.7860 - balanced_accuracy: 0.8567 - specificity: 0.9863 - miss_rate: 0.2729 - fall_out: 0.0137 - mcc: 0.7674 - val_loss: 0.5184 - val_accuracy: 0.8237 - val_recall: 0.7777 - val_precision: 0.8749 - val_AUROC: 0.9832 - val_AUPRC: 0.9084 - val_f1_score: 0.8234 - val_balanced_accuracy: 0.8827 - val_specificity: 0.9876 - val_miss_rate: 0.2223 - val_fall_out: 0.0124 - val_mcc: 0.8068
Epoch 17/100
63/63 [==============================] - 7s 105ms/step - loss: 0.5855 - accuracy: 0.8006 - recall: 0.7416 - precision: 0.8625 - AUROC: 0.9790 - AUPRC: 0.8859 - f1_score: 0.7975 - balanced_accuracy: 0.8642 - specificity: 0.9869 - miss_rate: 0.2584 - fall_out: 0.0131 - mcc: 0.7796 - val_loss: 0.4958 - val_accuracy: 0.8363 - val_recall: 0.7872 - val_precision: 0.8841 - val_AUROC: 0.9846 - val_AUPRC: 0.9170 - val_f1_score: 0.8328 - val_balanced_accuracy: 0.8879 - val_specificity: 0.9885 - val_miss_rate: 0.2128 - val_fall_out: 0.0115 - val_mcc: 0.8171
Epoch 18/100
63/63 [==============================] - 7s 104ms/step - loss: 0.5578 - accuracy: 0.8143 - recall: 0.7575 - precision: 0.8657 - AUROC: 0.9806 - AUPRC: 0.8945 - f1_score: 0.8080 - balanced_accuracy: 0.8722 - specificity: 0.9869 - miss_rate: 0.2425 - fall_out: 0.0131 - mcc: 0.7904 - val_loss: 0.4619 - val_accuracy: 0.8508 - val_recall: 0.8032 - val_precision: 0.8931 - val_AUROC: 0.9860 - val_AUPRC: 0.9244 - val_f1_score: 0.8458 - val_balanced_accuracy: 0.8963 - val_specificity: 0.9893 - val_miss_rate: 0.1968 - val_fall_out: 0.0107 - val_mcc: 0.8311
Epoch 19/100
63/63 [==============================] - 7s 106ms/step - loss: 0.5266 - accuracy: 0.8230 - recall: 0.7690 - precision: 0.8764 - AUROC: 0.9824 - AUPRC: 0.9038 - f1_score: 0.8192 - balanced_accuracy: 0.8785 - specificity: 0.9879 - miss_rate: 0.2310 - fall_out: 0.0121 - mcc: 0.8027 - val_loss: 0.4537 - val_accuracy: 0.8458 - val_recall: 0.8112 - val_precision: 0.8911 - val_AUROC: 0.9868 - val_AUPRC: 0.9283 - val_f1_score: 0.8493 - val_balanced_accuracy: 0.9001 - val_specificity: 0.9890 - val_miss_rate: 0.1888 - val_fall_out: 0.0110 - val_mcc: 0.8345
Epoch 20/100
63/63 [==============================] - 7s 105ms/step - loss: 0.4984 - accuracy: 0.8324 - recall: 0.7881 - precision: 0.8749 - AUROC: 0.9839 - AUPRC: 0.9110 - f1_score: 0.8292 - balanced_accuracy: 0.8878 - specificity: 0.9875 - miss_rate: 0.2119 - fall_out: 0.0125 - mcc: 0.8127 - val_loss: 0.4262 - val_accuracy: 0.8648 - val_recall: 0.8312 - val_precision: 0.9046 - val_AUROC: 0.9885 - val_AUPRC: 0.9375 - val_f1_score: 0.8664 - val_balanced_accuracy: 0.9108 - val_specificity: 0.9903 - val_miss_rate: 0.1688 - val_fall_out: 0.0097 - val_mcc: 0.8532
Epoch 21/100
63/63 [==============================] - 7s 103ms/step - loss: 0.4484 - accuracy: 0.8491 - recall: 0.8104 - precision: 0.8911 - AUROC: 0.9864 - AUPRC: 0.9260 - f1_score: 0.8488 - balanced_accuracy: 0.8997 - specificity: 0.9890 - miss_rate: 0.1896 - fall_out: 0.0110 - mcc: 0.8340 - val_loss: 0.4467 - val_accuracy: 0.8488 - val_recall: 0.8142 - val_precision: 0.8851 - val_AUROC: 0.9869 - val_AUPRC: 0.9312 - val_f1_score: 0.8482 - val_balanced_accuracy: 0.9012 - val_specificity: 0.9883 - val_miss_rate: 0.1858 - val_fall_out: 0.0117 - val_mcc: 0.8330
Epoch 22/100
63/63 [==============================] - 7s 104ms/step - loss: 0.4179 - accuracy: 0.8605 - recall: 0.8248 - precision: 0.8947 - AUROC: 0.9884 - AUPRC: 0.9356 - f1_score: 0.8583 - balanced_accuracy: 0.9070 - specificity: 0.9892 - miss_rate: 0.1752 - fall_out: 0.0108 - mcc: 0.8441 - val_loss: 0.4270 - val_accuracy: 0.8668 - val_recall: 0.8418 - val_precision: 0.9004 - val_AUROC: 0.9866 - val_AUPRC: 0.9349 - val_f1_score: 0.8701 - val_balanced_accuracy: 0.9157 - val_specificity: 0.9897 - val_miss_rate: 0.1582 - val_fall_out: 0.0103 - val_mcc: 0.8568
63/63 [==============================] - 4s 61ms/step - loss: 0.1948 - accuracy: 0.9426 - recall: 0.9216 - precision: 0.9628 - AUROC: 0.9980 - AUPRC: 0.9862 - f1_score: 0.9418 - balanced_accuracy: 0.9588 - specificity: 0.9960 - miss_rate: 0.0784 - fall_out: 0.0040 - mcc: 0.9357
16/16 [==============================] - 3s 180ms/step - loss: 0.4270 - accuracy: 0.8668 - recall: 0.8418 - precision: 0.9004 - AUROC: 0.9866 - AUPRC: 0.9349 - f1_score: 0.8701 - balanced_accuracy: 0.9157 - specificity: 0.9897 - miss_rate: 0.1582 - fall_out: 0.0103 - mcc: 0.8568
-- HOLDOUT 5 --
-- 6 Uncorrelated features: [Pearson+Spearman]
['mfcc10_var', 'mfcc17_mean', 'mfcc16_mean', 'mfcc11_var', 'tempo', 'mfcc19_mean']
-- Actually uncorrelated features: [confirmed via MIC]
[]
-- 1 Correlated features: [Pearson]
['spectral_centroid_mean']
- Removing uncorrelated/correlated features: ['spectral_centroid_mean']
- Building Fixed MLP:
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
features_data (InputLayer) [(None, 56)] 0
dense (Dense) (None, 256) 14592
dropout (Dropout) (None, 256) 0
dense_1 (Dense) (None, 256) 65792
dropout_1 (Dropout) (None, 256) 0
dense_2 (Dense) (None, 128) 32896
dropout_2 (Dropout) (None, 128) 0
dense_3 (Dense) (None, 128) 16512
dropout_3 (Dropout) (None, 128) 0
dense_4 (Dense) (None, 10) 1290
=================================================================
Total params: 131,082
Trainable params: 131,082
Non-trainable params: 0
_________________________________________________________________
- Building Fixed CNN:
Model: "model_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
mfccs_data (InputLayer) [(None, 20, 130, 1)] 0
conv2d (Conv2D) (None, 20, 130, 256) 2560
max_pooling2d (MaxPooling2D (None, 10, 65, 256) 0
)
dropout_4 (Dropout) (None, 10, 65, 256) 0
conv2d_1 (Conv2D) (None, 10, 65, 256) 590080
max_pooling2d_1 (MaxPooling (None, 5, 33, 256) 0
2D)
dropout_5 (Dropout) (None, 5, 33, 256) 0
conv2d_2 (Conv2D) (None, 5, 33, 256) 590080
max_pooling2d_2 (MaxPooling (None, 3, 17, 256) 0
2D)
dropout_6 (Dropout) (None, 3, 17, 256) 0
conv2d_3 (Conv2D) (None, 3, 17, 512) 2097664
max_pooling2d_3 (MaxPooling (None, 2, 9, 512) 0
2D)
flatten (Flatten) (None, 9216) 0
dense_5 (Dense) (None, 128) 1179776
dropout_7 (Dropout) (None, 128) 0
dense_6 (Dense) (None, 128) 16512
dropout_8 (Dropout) (None, 128) 0
dense_7 (Dense) (None, 10) 1290
=================================================================
Total params: 4,477,962
Trainable params: 4,477,962
Non-trainable params: 0
_________________________________________________________________
- Building MMNN:
Model: "model_2"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
mfccs_data (InputLayer) [(None, 20, 130, 1) 0 []
]
conv2d (Conv2D) (None, 20, 130, 256 2560 ['mfccs_data[0][0]']
)
max_pooling2d (MaxPooling2D) (None, 10, 65, 256) 0 ['conv2d[0][0]']
dropout_4 (Dropout) (None, 10, 65, 256) 0 ['max_pooling2d[0][0]']
conv2d_1 (Conv2D) (None, 10, 65, 256) 590080 ['dropout_4[0][0]']
max_pooling2d_1 (MaxPooling2D) (None, 5, 33, 256) 0 ['conv2d_1[0][0]']
dropout_5 (Dropout) (None, 5, 33, 256) 0 ['max_pooling2d_1[0][0]']
conv2d_2 (Conv2D) (None, 5, 33, 256) 590080 ['dropout_5[0][0]']
features_data (InputLayer) [(None, 56)] 0 []
max_pooling2d_2 (MaxPooling2D) (None, 3, 17, 256) 0 ['conv2d_2[0][0]']
dense (Dense) (None, 256) 14592 ['features_data[0][0]']
dropout_6 (Dropout) (None, 3, 17, 256) 0 ['max_pooling2d_2[0][0]']
dropout (Dropout) (None, 256) 0 ['dense[0][0]']
conv2d_3 (Conv2D) (None, 3, 17, 512) 2097664 ['dropout_6[0][0]']
dense_1 (Dense) (None, 256) 65792 ['dropout[0][0]']
max_pooling2d_3 (MaxPooling2D) (None, 2, 9, 512) 0 ['conv2d_3[0][0]']
dropout_1 (Dropout) (None, 256) 0 ['dense_1[0][0]']
flatten (Flatten) (None, 9216) 0 ['max_pooling2d_3[0][0]']
dense_2 (Dense) (None, 128) 32896 ['dropout_1[0][0]']
dense_5 (Dense) (None, 128) 1179776 ['flatten[0][0]']
dropout_2 (Dropout) (None, 128) 0 ['dense_2[0][0]']
dropout_7 (Dropout) (None, 128) 0 ['dense_5[0][0]']
dense_3 (Dense) (None, 128) 16512 ['dropout_2[0][0]']
dense_6 (Dense) (None, 128) 16512 ['dropout_7[0][0]']
dropout_3 (Dropout) (None, 128) 0 ['dense_3[0][0]']
dropout_8 (Dropout) (None, 128) 0 ['dense_6[0][0]']
concatenate (Concatenate) (None, 256) 0 ['dropout_3[0][0]',
'dropout_8[0][0]']
dense_8 (Dense) (None, 64) 16448 ['concatenate[0][0]']
dense_9 (Dense) (None, 10) 650 ['dense_8[0][0]']
==================================================================================================
Total params: 4,623,562
Trainable params: 4,623,562
Non-trainable params: 0
__________________________________________________________________________________________________
- Training MMNN model:
Epoch 1/100
63/63 [==============================] - 9s 110ms/step - loss: 2.2258 - accuracy: 0.1949 - recall: 0.0177 - precision: 0.4029 - AUROC: 0.6428 - AUPRC: 0.1786 - f1_score: 0.0338 - balanced_accuracy: 0.5074 - specificity: 0.9971 - miss_rate: 0.9823 - fall_out: 0.0029 - mcc: 0.0670 - val_loss: 1.6521 - val_accuracy: 0.3505 - val_recall: 0.1602 - val_precision: 0.8247 - val_AUROC: 0.8476 - val_AUPRC: 0.4442 - val_f1_score: 0.2683 - val_balanced_accuracy: 0.5782 - val_specificity: 0.9962 - val_miss_rate: 0.8398 - val_fall_out: 0.0038 - val_mcc: 0.3401
Epoch 2/100
63/63 [==============================] - 7s 105ms/step - loss: 1.6958 - accuracy: 0.3766 - recall: 0.1667 - precision: 0.6480 - AUROC: 0.8285 - AUPRC: 0.3965 - f1_score: 0.2652 - balanced_accuracy: 0.5783 - specificity: 0.9899 - miss_rate: 0.8333 - fall_out: 0.0101 - mcc: 0.2968 - val_loss: 1.3858 - val_accuracy: 0.5008 - val_recall: 0.2479 - val_precision: 0.8088 - val_AUROC: 0.8999 - val_AUPRC: 0.5652 - val_f1_score: 0.3795 - val_balanced_accuracy: 0.6207 - val_specificity: 0.9935 - val_miss_rate: 0.7521 - val_fall_out: 0.0065 - val_mcc: 0.4201
Epoch 3/100
63/63 [==============================] - 7s 105ms/step - loss: 1.4940 - accuracy: 0.4614 - recall: 0.2365 - precision: 0.6883 - AUROC: 0.8746 - AUPRC: 0.4952 - f1_score: 0.3520 - balanced_accuracy: 0.6123 - specificity: 0.9881 - miss_rate: 0.7635 - fall_out: 0.0119 - mcc: 0.3699 - val_loss: 1.1925 - val_accuracy: 0.5658 - val_recall: 0.3295 - val_precision: 0.7714 - val_AUROC: 0.9239 - val_AUPRC: 0.6392 - val_f1_score: 0.4618 - val_balanced_accuracy: 0.6593 - val_specificity: 0.9892 - val_miss_rate: 0.6705 - val_fall_out: 0.0108 - val_mcc: 0.4727
Epoch 4/100
63/63 [==============================] - 7s 105ms/step - loss: 1.3389 - accuracy: 0.5168 - recall: 0.3030 - precision: 0.7063 - AUROC: 0.9000 - AUPRC: 0.5634 - f1_score: 0.4241 - balanced_accuracy: 0.6445 - specificity: 0.9860 - miss_rate: 0.6970 - fall_out: 0.0140 - mcc: 0.4278 - val_loss: 1.0836 - val_accuracy: 0.6169 - val_recall: 0.4006 - val_precision: 0.8138 - val_AUROC: 0.9378 - val_AUPRC: 0.6942 - val_f1_score: 0.5369 - val_balanced_accuracy: 0.6952 - val_specificity: 0.9898 - val_miss_rate: 0.5994 - val_fall_out: 0.0102 - val_mcc: 0.5414
Epoch 5/100
63/63 [==============================] - 7s 105ms/step - loss: 1.2225 - accuracy: 0.5636 - recall: 0.3700 - precision: 0.7376 - AUROC: 0.9158 - AUPRC: 0.6245 - f1_score: 0.4928 - balanced_accuracy: 0.6777 - specificity: 0.9854 - miss_rate: 0.6300 - fall_out: 0.0146 - mcc: 0.4884 - val_loss: 0.9723 - val_accuracy: 0.6680 - val_recall: 0.4812 - val_precision: 0.8249 - val_AUROC: 0.9499 - val_AUPRC: 0.7447 - val_f1_score: 0.6078 - val_balanced_accuracy: 0.7349 - val_specificity: 0.9886 - val_miss_rate: 0.5188 - val_fall_out: 0.0114 - val_mcc: 0.6014
Epoch 6/100
63/63 [==============================] - 7s 105ms/step - loss: 1.1386 - accuracy: 0.6056 - recall: 0.4257 - precision: 0.7532 - AUROC: 0.9275 - AUPRC: 0.6647 - f1_score: 0.5440 - balanced_accuracy: 0.7051 - specificity: 0.9845 - miss_rate: 0.5743 - fall_out: 0.0155 - mcc: 0.5329 - val_loss: 0.8928 - val_accuracy: 0.6895 - val_recall: 0.5373 - val_precision: 0.8357 - val_AUROC: 0.9567 - val_AUPRC: 0.7779 - val_f1_score: 0.6541 - val_balanced_accuracy: 0.7628 - val_specificity: 0.9883 - val_miss_rate: 0.4627 - val_fall_out: 0.0117 - val_mcc: 0.6428
Epoch 7/100
63/63 [==============================] - 7s 105ms/step - loss: 1.0711 - accuracy: 0.6251 - recall: 0.4649 - precision: 0.7681 - AUROC: 0.9354 - AUPRC: 0.6955 - f1_score: 0.5792 - balanced_accuracy: 0.7247 - specificity: 0.9844 - miss_rate: 0.5351 - fall_out: 0.0156 - mcc: 0.5653 - val_loss: 0.8307 - val_accuracy: 0.7191 - val_recall: 0.5679 - val_precision: 0.8475 - val_AUROC: 0.9624 - val_AUPRC: 0.8041 - val_f1_score: 0.6801 - val_balanced_accuracy: 0.7783 - val_specificity: 0.9886 - val_miss_rate: 0.4321 - val_fall_out: 0.0114 - val_mcc: 0.6677
Epoch 8/100
63/63 [==============================] - 7s 105ms/step - loss: 0.9935 - accuracy: 0.6655 - recall: 0.5130 - precision: 0.7906 - AUROC: 0.9437 - AUPRC: 0.7346 - f1_score: 0.6223 - balanced_accuracy: 0.7490 - specificity: 0.9849 - miss_rate: 0.4870 - fall_out: 0.0151 - mcc: 0.6064 - val_loss: 0.7849 - val_accuracy: 0.7316 - val_recall: 0.5919 - val_precision: 0.8522 - val_AUROC: 0.9667 - val_AUPRC: 0.8224 - val_f1_score: 0.6986 - val_balanced_accuracy: 0.7902 - val_specificity: 0.9886 - val_miss_rate: 0.4081 - val_fall_out: 0.0114 - val_mcc: 0.6850
Epoch 9/100
63/63 [==============================] - 7s 105ms/step - loss: 0.9419 - accuracy: 0.6752 - recall: 0.5426 - precision: 0.7925 - AUROC: 0.9495 - AUPRC: 0.7547 - f1_score: 0.6442 - balanced_accuracy: 0.7634 - specificity: 0.9842 - miss_rate: 0.4574 - fall_out: 0.0158 - mcc: 0.6258 - val_loss: 0.7503 - val_accuracy: 0.7461 - val_recall: 0.6104 - val_precision: 0.8591 - val_AUROC: 0.9695 - val_AUPRC: 0.8352 - val_f1_score: 0.7137 - val_balanced_accuracy: 0.7996 - val_specificity: 0.9889 - val_miss_rate: 0.3896 - val_fall_out: 0.0111 - val_mcc: 0.6998
Epoch 10/100
63/63 [==============================] - 7s 105ms/step - loss: 0.8827 - accuracy: 0.6930 - recall: 0.5792 - precision: 0.8011 - AUROC: 0.9551 - AUPRC: 0.7761 - f1_score: 0.6723 - balanced_accuracy: 0.7816 - specificity: 0.9840 - miss_rate: 0.4208 - fall_out: 0.0160 - mcc: 0.6524 - val_loss: 0.7229 - val_accuracy: 0.7496 - val_recall: 0.6304 - val_precision: 0.8576 - val_AUROC: 0.9716 - val_AUPRC: 0.8449 - val_f1_score: 0.7267 - val_balanced_accuracy: 0.8094 - val_specificity: 0.9884 - val_miss_rate: 0.3696 - val_fall_out: 0.0116 - val_mcc: 0.7114
Epoch 11/100
63/63 [==============================] - 7s 105ms/step - loss: 0.8530 - accuracy: 0.7112 - recall: 0.5947 - precision: 0.8100 - AUROC: 0.9577 - AUPRC: 0.7888 - f1_score: 0.6858 - balanced_accuracy: 0.7896 - specificity: 0.9845 - miss_rate: 0.4053 - fall_out: 0.0155 - mcc: 0.6662 - val_loss: 0.6636 - val_accuracy: 0.7752 - val_recall: 0.6830 - val_precision: 0.8622 - val_AUROC: 0.9754 - val_AUPRC: 0.8646 - val_f1_score: 0.7622 - val_balanced_accuracy: 0.8354 - val_specificity: 0.9879 - val_miss_rate: 0.3170 - val_fall_out: 0.0121 - val_mcc: 0.7452
Epoch 12/100
63/63 [==============================] - 7s 105ms/step - loss: 0.8062 - accuracy: 0.7270 - recall: 0.6300 - precision: 0.8194 - AUROC: 0.9620 - AUPRC: 0.8082 - f1_score: 0.7123 - balanced_accuracy: 0.8073 - specificity: 0.9846 - miss_rate: 0.3700 - fall_out: 0.0154 - mcc: 0.6920 - val_loss: 0.6559 - val_accuracy: 0.7712 - val_recall: 0.6905 - val_precision: 0.8497 - val_AUROC: 0.9754 - val_AUPRC: 0.8653 - val_f1_score: 0.7619 - val_balanced_accuracy: 0.8385 - val_specificity: 0.9864 - val_miss_rate: 0.3095 - val_fall_out: 0.0136 - val_mcc: 0.7432
Epoch 13/100
63/63 [==============================] - 7s 105ms/step - loss: 0.7688 - accuracy: 0.7410 - recall: 0.6482 - precision: 0.8315 - AUROC: 0.9653 - AUPRC: 0.8235 - f1_score: 0.7285 - balanced_accuracy: 0.8168 - specificity: 0.9854 - miss_rate: 0.3518 - fall_out: 0.0146 - mcc: 0.7090 - val_loss: 0.6223 - val_accuracy: 0.7937 - val_recall: 0.7036 - val_precision: 0.8641 - val_AUROC: 0.9774 - val_AUPRC: 0.8782 - val_f1_score: 0.7756 - val_balanced_accuracy: 0.8456 - val_specificity: 0.9877 - val_miss_rate: 0.2964 - val_fall_out: 0.0123 - val_mcc: 0.7583
Epoch 14/100
63/63 [==============================] - 7s 105ms/step - loss: 0.7207 - accuracy: 0.7549 - recall: 0.6692 - precision: 0.8343 - AUROC: 0.9691 - AUPRC: 0.8394 - f1_score: 0.7427 - balanced_accuracy: 0.8272 - specificity: 0.9852 - miss_rate: 0.3308 - fall_out: 0.0148 - mcc: 0.7228 - val_loss: 0.5896 - val_accuracy: 0.8107 - val_recall: 0.7191 - val_precision: 0.8735 - val_AUROC: 0.9796 - val_AUPRC: 0.8889 - val_f1_score: 0.7888 - val_balanced_accuracy: 0.8538 - val_specificity: 0.9884 - val_miss_rate: 0.2809 - val_fall_out: 0.0116 - val_mcc: 0.7722
Epoch 15/100
63/63 [==============================] - 7s 105ms/step - loss: 0.7107 - accuracy: 0.7611 - recall: 0.6851 - precision: 0.8364 - AUROC: 0.9700 - AUPRC: 0.8440 - f1_score: 0.7532 - balanced_accuracy: 0.8351 - specificity: 0.9851 - miss_rate: 0.3149 - fall_out: 0.0149 - mcc: 0.7332 - val_loss: 0.5644 - val_accuracy: 0.8017 - val_recall: 0.7386 - val_precision: 0.8712 - val_AUROC: 0.9814 - val_AUPRC: 0.8952 - val_f1_score: 0.7995 - val_balanced_accuracy: 0.8632 - val_specificity: 0.9879 - val_miss_rate: 0.2614 - val_fall_out: 0.0121 - val_mcc: 0.7824
Epoch 16/100
63/63 [==============================] - 7s 105ms/step - loss: 0.6785 - accuracy: 0.7672 - recall: 0.6990 - precision: 0.8399 - AUROC: 0.9722 - AUPRC: 0.8538 - f1_score: 0.7630 - balanced_accuracy: 0.8421 - specificity: 0.9852 - miss_rate: 0.3010 - fall_out: 0.0148 - mcc: 0.7431 - val_loss: 0.5486 - val_accuracy: 0.8127 - val_recall: 0.7491 - val_precision: 0.8688 - val_AUROC: 0.9822 - val_AUPRC: 0.9001 - val_f1_score: 0.8045 - val_balanced_accuracy: 0.8683 - val_specificity: 0.9874 - val_miss_rate: 0.2509 - val_fall_out: 0.0126 - val_mcc: 0.7872
Epoch 17/100
63/63 [==============================] - 7s 105ms/step - loss: 0.6343 - accuracy: 0.7863 - recall: 0.7229 - precision: 0.8525 - AUROC: 0.9749 - AUPRC: 0.8718 - f1_score: 0.7824 - balanced_accuracy: 0.8545 - specificity: 0.9861 - miss_rate: 0.2771 - fall_out: 0.0139 - mcc: 0.7635 - val_loss: 0.5228 - val_accuracy: 0.8247 - val_recall: 0.7661 - val_precision: 0.8839 - val_AUROC: 0.9837 - val_AUPRC: 0.9085 - val_f1_score: 0.8208 - val_balanced_accuracy: 0.8775 - val_specificity: 0.9888 - val_miss_rate: 0.2339 - val_fall_out: 0.0112 - val_mcc: 0.8050
Epoch 18/100
63/63 [==============================] - 7s 105ms/step - loss: 0.5976 - accuracy: 0.8041 - recall: 0.7390 - precision: 0.8568 - AUROC: 0.9779 - AUPRC: 0.8808 - f1_score: 0.7935 - balanced_accuracy: 0.8626 - specificity: 0.9863 - miss_rate: 0.2610 - fall_out: 0.0137 - mcc: 0.7750 - val_loss: 0.5115 - val_accuracy: 0.8267 - val_recall: 0.7767 - val_precision: 0.8743 - val_AUROC: 0.9845 - val_AUPRC: 0.9109 - val_f1_score: 0.8226 - val_balanced_accuracy: 0.8821 - val_specificity: 0.9876 - val_miss_rate: 0.2233 - val_fall_out: 0.0124 - val_mcc: 0.8059
Epoch 19/100
63/63 [==============================] - 7s 106ms/step - loss: 0.5695 - accuracy: 0.8095 - recall: 0.7568 - precision: 0.8652 - AUROC: 0.9798 - AUPRC: 0.8918 - f1_score: 0.8074 - balanced_accuracy: 0.8718 - specificity: 0.9869 - miss_rate: 0.2432 - fall_out: 0.0131 - mcc: 0.7897 - val_loss: 0.5024 - val_accuracy: 0.8302 - val_recall: 0.7837 - val_precision: 0.8892 - val_AUROC: 0.9848 - val_AUPRC: 0.9134 - val_f1_score: 0.8331 - val_balanced_accuracy: 0.8864 - val_specificity: 0.9892 - val_miss_rate: 0.2163 - val_fall_out: 0.0108 - val_mcc: 0.8178
Epoch 20/100
63/63 [==============================] - 7s 106ms/step - loss: 0.5522 - accuracy: 0.8173 - recall: 0.7650 - precision: 0.8705 - AUROC: 0.9809 - AUPRC: 0.8978 - f1_score: 0.8143 - balanced_accuracy: 0.8762 - specificity: 0.9873 - miss_rate: 0.2350 - fall_out: 0.0127 - mcc: 0.7972 - val_loss: 0.4843 - val_accuracy: 0.8403 - val_recall: 0.7867 - val_precision: 0.8846 - val_AUROC: 0.9858 - val_AUPRC: 0.9186 - val_f1_score: 0.8328 - val_balanced_accuracy: 0.8876 - val_specificity: 0.9886 - val_miss_rate: 0.2133 - val_fall_out: 0.0114 - val_mcc: 0.8171
Epoch 21/100
63/63 [==============================] - 7s 105ms/step - loss: 0.5367 - accuracy: 0.8268 - recall: 0.7762 - precision: 0.8716 - AUROC: 0.9814 - AUPRC: 0.9016 - f1_score: 0.8211 - balanced_accuracy: 0.8817 - specificity: 0.9873 - miss_rate: 0.2238 - fall_out: 0.0127 - mcc: 0.8042 - val_loss: 0.4583 - val_accuracy: 0.8488 - val_recall: 0.8072 - val_precision: 0.8872 - val_AUROC: 0.9865 - val_AUPRC: 0.9256 - val_f1_score: 0.8453 - val_balanced_accuracy: 0.8979 - val_specificity: 0.9886 - val_miss_rate: 0.1928 - val_fall_out: 0.0114 - val_mcc: 0.8301
Epoch 22/100
63/63 [==============================] - 7s 105ms/step - loss: 0.4909 - accuracy: 0.8350 - recall: 0.7911 - precision: 0.8791 - AUROC: 0.9849 - AUPRC: 0.9144 - f1_score: 0.8328 - balanced_accuracy: 0.8895 - specificity: 0.9879 - miss_rate: 0.2089 - fall_out: 0.0121 - mcc: 0.8166 - val_loss: 0.4466 - val_accuracy: 0.8458 - val_recall: 0.8077 - val_precision: 0.8902 - val_AUROC: 0.9873 - val_AUPRC: 0.9280 - val_f1_score: 0.8469 - val_balanced_accuracy: 0.8983 - val_specificity: 0.9889 - val_miss_rate: 0.1923 - val_fall_out: 0.0111 - val_mcc: 0.8320
Epoch 23/100
63/63 [==============================] - 7s 105ms/step - loss: 0.4819 - accuracy: 0.8427 - recall: 0.8041 - precision: 0.8860 - AUROC: 0.9847 - AUPRC: 0.9182 - f1_score: 0.8431 - balanced_accuracy: 0.8963 - specificity: 0.9885 - miss_rate: 0.1959 - fall_out: 0.0115 - mcc: 0.8278 - val_loss: 0.4426 - val_accuracy: 0.8528 - val_recall: 0.8132 - val_precision: 0.8928 - val_AUROC: 0.9864 - val_AUPRC: 0.9295 - val_f1_score: 0.8512 - val_balanced_accuracy: 0.9012 - val_specificity: 0.9892 - val_miss_rate: 0.1868 - val_fall_out: 0.0108 - val_mcc: 0.8366
Epoch 24/100
63/63 [==============================] - 7s 105ms/step - loss: 0.4530 - accuracy: 0.8498 - recall: 0.8151 - precision: 0.8937 - AUROC: 0.9866 - AUPRC: 0.9261 - f1_score: 0.8526 - balanced_accuracy: 0.9022 - specificity: 0.9892 - miss_rate: 0.1849 - fall_out: 0.0108 - mcc: 0.8382 - val_loss: 0.4453 - val_accuracy: 0.8588 - val_recall: 0.8147 - val_precision: 0.9014 - val_AUROC: 0.9876 - val_AUPRC: 0.9296 - val_f1_score: 0.8559 - val_balanced_accuracy: 0.9024 - val_specificity: 0.9901 - val_miss_rate: 0.1853 - val_fall_out: 0.0099 - val_mcc: 0.8421
Epoch 25/100
63/63 [==============================] - 7s 106ms/step - loss: 0.4312 - accuracy: 0.8593 - recall: 0.8259 - precision: 0.8964 - AUROC: 0.9870 - AUPRC: 0.9300 - f1_score: 0.8597 - balanced_accuracy: 0.9076 - specificity: 0.9894 - miss_rate: 0.1741 - fall_out: 0.0106 - mcc: 0.8457 - val_loss: 0.4126 - val_accuracy: 0.8683 - val_recall: 0.8348 - val_precision: 0.9016 - val_AUROC: 0.9883 - val_AUPRC: 0.9379 - val_f1_score: 0.8669 - val_balanced_accuracy: 0.9123 - val_specificity: 0.9899 - val_miss_rate: 0.1652 - val_fall_out: 0.0101 - val_mcc: 0.8535
Epoch 26/100
63/63 [==============================] - 7s 106ms/step - loss: 0.4125 - accuracy: 0.8629 - recall: 0.8285 - precision: 0.9005 - AUROC: 0.9879 - AUPRC: 0.9350 - f1_score: 0.8630 - balanced_accuracy: 0.9092 - specificity: 0.9898 - miss_rate: 0.1715 - fall_out: 0.0102 - mcc: 0.8494 - val_loss: 0.4004 - val_accuracy: 0.8763 - val_recall: 0.8518 - val_precision: 0.9010 - val_AUROC: 0.9882 - val_AUPRC: 0.9393 - val_f1_score: 0.8757 - val_balanced_accuracy: 0.9207 - val_specificity: 0.9896 - val_miss_rate: 0.1482 - val_fall_out: 0.0104 - val_mcc: 0.8627
Epoch 27/100
63/63 [==============================] - 7s 105ms/step - loss: 0.4082 - accuracy: 0.8655 - recall: 0.8314 - precision: 0.9001 - AUROC: 0.9886 - AUPRC: 0.9370 - f1_score: 0.8644 - balanced_accuracy: 0.9106 - specificity: 0.9897 - miss_rate: 0.1686 - fall_out: 0.0103 - mcc: 0.8508 - val_loss: 0.4038 - val_accuracy: 0.8728 - val_recall: 0.8503 - val_precision: 0.9008 - val_AUROC: 0.9879 - val_AUPRC: 0.9384 - val_f1_score: 0.8748 - val_balanced_accuracy: 0.9199 - val_specificity: 0.9896 - val_miss_rate: 0.1497 - val_fall_out: 0.0104 - val_mcc: 0.8618
Epoch 28/100
63/63 [==============================] - 7s 105ms/step - loss: 0.3758 - accuracy: 0.8747 - recall: 0.8461 - precision: 0.9048 - AUROC: 0.9905 - AUPRC: 0.9452 - f1_score: 0.8744 - balanced_accuracy: 0.9181 - specificity: 0.9901 - miss_rate: 0.1539 - fall_out: 0.0099 - mcc: 0.8616 - val_loss: 0.3977 - val_accuracy: 0.8798 - val_recall: 0.8553 - val_precision: 0.9071 - val_AUROC: 0.9880 - val_AUPRC: 0.9402 - val_f1_score: 0.8804 - val_balanced_accuracy: 0.9228 - val_specificity: 0.9903 - val_miss_rate: 0.1447 - val_fall_out: 0.0097 - val_mcc: 0.8680
Epoch 29/100
63/63 [==============================] - 7s 105ms/step - loss: 0.3648 - accuracy: 0.8788 - recall: 0.8525 - precision: 0.9063 - AUROC: 0.9908 - AUPRC: 0.9465 - f1_score: 0.8785 - balanced_accuracy: 0.9213 - specificity: 0.9902 - miss_rate: 0.1475 - fall_out: 0.0098 - mcc: 0.8660 - val_loss: 0.4050 - val_accuracy: 0.8783 - val_recall: 0.8563 - val_precision: 0.8953 - val_AUROC: 0.9873 - val_AUPRC: 0.9386 - val_f1_score: 0.8754 - val_balanced_accuracy: 0.9226 - val_specificity: 0.9889 - val_miss_rate: 0.1437 - val_fall_out: 0.0111 - val_mcc: 0.8621
Epoch 30/100
63/63 [==============================] - 7s 105ms/step - loss: 0.3641 - accuracy: 0.8834 - recall: 0.8561 - precision: 0.9076 - AUROC: 0.9900 - AUPRC: 0.9476 - f1_score: 0.8811 - balanced_accuracy: 0.9232 - specificity: 0.9903 - miss_rate: 0.1439 - fall_out: 0.0097 - mcc: 0.8688 - val_loss: 0.3893 - val_accuracy: 0.8773 - val_recall: 0.8563 - val_precision: 0.9115 - val_AUROC: 0.9879 - val_AUPRC: 0.9428 - val_f1_score: 0.8830 - val_balanced_accuracy: 0.9235 - val_specificity: 0.9908 - val_miss_rate: 0.1437 - val_fall_out: 0.0092 - val_mcc: 0.8710
Epoch 31/100
63/63 [==============================] - 7s 106ms/step - loss: 0.3399 - accuracy: 0.8805 - recall: 0.8576 - precision: 0.9085 - AUROC: 0.9916 - AUPRC: 0.9536 - f1_score: 0.8823 - balanced_accuracy: 0.9240 - specificity: 0.9904 - miss_rate: 0.1424 - fall_out: 0.0096 - mcc: 0.8701 - val_loss: 0.3847 - val_accuracy: 0.8718 - val_recall: 0.8518 - val_precision: 0.8995 - val_AUROC: 0.9888 - val_AUPRC: 0.9430 - val_f1_score: 0.8750 - val_balanced_accuracy: 0.9206 - val_specificity: 0.9894 - val_miss_rate: 0.1482 - val_fall_out: 0.0106 - val_mcc: 0.8619
Epoch 32/100
63/63 [==============================] - 7s 106ms/step - loss: 0.3314 - accuracy: 0.8893 - recall: 0.8684 - precision: 0.9127 - AUROC: 0.9919 - AUPRC: 0.9549 - f1_score: 0.8900 - balanced_accuracy: 0.9296 - specificity: 0.9908 - miss_rate: 0.1316 - fall_out: 0.0092 - mcc: 0.8784 - val_loss: 0.4217 - val_accuracy: 0.8568 - val_recall: 0.8332 - val_precision: 0.8870 - val_AUROC: 0.9878 - val_AUPRC: 0.9359 - val_f1_score: 0.8593 - val_balanced_accuracy: 0.9107 - val_specificity: 0.9882 - val_miss_rate: 0.1668 - val_fall_out: 0.0118 - val_mcc: 0.8447
Epoch 33/100
63/63 [==============================] - 7s 107ms/step - loss: 0.3205 - accuracy: 0.8962 - recall: 0.8754 - precision: 0.9191 - AUROC: 0.9923 - AUPRC: 0.9579 - f1_score: 0.8967 - balanced_accuracy: 0.9334 - specificity: 0.9914 - miss_rate: 0.1246 - fall_out: 0.0086 - mcc: 0.8859 - val_loss: 0.3683 - val_accuracy: 0.8818 - val_recall: 0.8608 - val_precision: 0.9086 - val_AUROC: 0.9900 - val_AUPRC: 0.9474 - val_f1_score: 0.8840 - val_balanced_accuracy: 0.9256 - val_specificity: 0.9904 - val_miss_rate: 0.1392 - val_fall_out: 0.0096 - val_mcc: 0.8719
Epoch 34/100
63/63 [==============================] - 7s 106ms/step - loss: 0.2949 - accuracy: 0.9031 - recall: 0.8835 - precision: 0.9229 - AUROC: 0.9933 - AUPRC: 0.9626 - f1_score: 0.9028 - balanced_accuracy: 0.9377 - specificity: 0.9918 - miss_rate: 0.1165 - fall_out: 0.0082 - mcc: 0.8925 - val_loss: 0.3350 - val_accuracy: 0.8973 - val_recall: 0.8773 - val_precision: 0.9120 - val_AUROC: 0.9909 - val_AUPRC: 0.9553 - val_f1_score: 0.8943 - val_balanced_accuracy: 0.9340 - val_specificity: 0.9906 - val_miss_rate: 0.1227 - val_fall_out: 0.0094 - val_mcc: 0.8830
Epoch 35/100
63/63 [==============================] - 7s 105ms/step - loss: 0.2885 - accuracy: 0.9039 - recall: 0.8854 - precision: 0.9234 - AUROC: 0.9932 - AUPRC: 0.9643 - f1_score: 0.9040 - balanced_accuracy: 0.9386 - specificity: 0.9918 - miss_rate: 0.1146 - fall_out: 0.0082 - mcc: 0.8938 - val_loss: 0.3560 - val_accuracy: 0.8893 - val_recall: 0.8738 - val_precision: 0.9089 - val_AUROC: 0.9891 - val_AUPRC: 0.9516 - val_f1_score: 0.8910 - val_balanced_accuracy: 0.9320 - val_specificity: 0.9903 - val_miss_rate: 0.1262 - val_fall_out: 0.0097 - val_mcc: 0.8793
Epoch 36/100
63/63 [==============================] - 7s 105ms/step - loss: 0.2641 - accuracy: 0.9109 - recall: 0.8943 - precision: 0.9288 - AUROC: 0.9948 - AUPRC: 0.9697 - f1_score: 0.9112 - balanced_accuracy: 0.9433 - specificity: 0.9924 - miss_rate: 0.1057 - fall_out: 0.0076 - mcc: 0.9018 - val_loss: 0.3553 - val_accuracy: 0.8933 - val_recall: 0.8788 - val_precision: 0.9093 - val_AUROC: 0.9881 - val_AUPRC: 0.9490 - val_f1_score: 0.8938 - val_balanced_accuracy: 0.9345 - val_specificity: 0.9903 - val_miss_rate: 0.1212 - val_fall_out: 0.0097 - val_mcc: 0.8824
63/63 [==============================] - 4s 61ms/step - loss: 0.0763 - accuracy: 0.9785 - recall: 0.9722 - precision: 0.9833 - AUROC: 0.9995 - AUPRC: 0.9974 - f1_score: 0.9777 - balanced_accuracy: 0.9852 - specificity: 0.9982 - miss_rate: 0.0278 - fall_out: 0.0018 - mcc: 0.9753
16/16 [==============================] - 3s 178ms/step - loss: 0.3553 - accuracy: 0.8933 - recall: 0.8788 - precision: 0.9093 - AUROC: 0.9881 - AUPRC: 0.9490 - f1_score: 0.8938 - balanced_accuracy: 0.9345 - specificity: 0.9903 - miss_rate: 0.1212 - fall_out: 0.0097 - mcc: 0.8824
-- HOLDOUT 6 --
-- 5 Uncorrelated features: [Pearson+Spearman]
['mfcc10_var', 'mfcc17_mean', 'mfcc16_mean', 'tempo', 'mfcc19_mean']
-- Actually uncorrelated features: [confirmed via MIC]
[]
-- 1 Correlated features: [Pearson]
['spectral_centroid_mean']
- Removing uncorrelated/correlated features: ['spectral_centroid_mean']
- Building Fixed MLP:
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
features_data (InputLayer) [(None, 56)] 0
dense (Dense) (None, 256) 14592
dropout (Dropout) (None, 256) 0
dense_1 (Dense) (None, 256) 65792
dropout_1 (Dropout) (None, 256) 0
dense_2 (Dense) (None, 128) 32896
dropout_2 (Dropout) (None, 128) 0
dense_3 (Dense) (None, 128) 16512
dropout_3 (Dropout) (None, 128) 0
dense_4 (Dense) (None, 10) 1290
=================================================================
Total params: 131,082
Trainable params: 131,082
Non-trainable params: 0
_________________________________________________________________
- Building Fixed CNN:
Model: "model_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
mfccs_data (InputLayer) [(None, 20, 130, 1)] 0
conv2d (Conv2D) (None, 20, 130, 256) 2560
max_pooling2d (MaxPooling2D (None, 10, 65, 256) 0
)
dropout_4 (Dropout) (None, 10, 65, 256) 0
conv2d_1 (Conv2D) (None, 10, 65, 256) 590080
max_pooling2d_1 (MaxPooling (None, 5, 33, 256) 0
2D)
dropout_5 (Dropout) (None, 5, 33, 256) 0
conv2d_2 (Conv2D) (None, 5, 33, 256) 590080
max_pooling2d_2 (MaxPooling (None, 3, 17, 256) 0
2D)
dropout_6 (Dropout) (None, 3, 17, 256) 0
conv2d_3 (Conv2D) (None, 3, 17, 512) 2097664
max_pooling2d_3 (MaxPooling (None, 2, 9, 512) 0
2D)
flatten (Flatten) (None, 9216) 0
dense_5 (Dense) (None, 128) 1179776
dropout_7 (Dropout) (None, 128) 0
dense_6 (Dense) (None, 128) 16512
dropout_8 (Dropout) (None, 128) 0
dense_7 (Dense) (None, 10) 1290
=================================================================
Total params: 4,477,962
Trainable params: 4,477,962
Non-trainable params: 0
_________________________________________________________________
- Building MMNN:
Model: "model_2"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
mfccs_data (InputLayer) [(None, 20, 130, 1) 0 []
]
conv2d (Conv2D) (None, 20, 130, 256 2560 ['mfccs_data[0][0]']
)
max_pooling2d (MaxPooling2D) (None, 10, 65, 256) 0 ['conv2d[0][0]']
dropout_4 (Dropout) (None, 10, 65, 256) 0 ['max_pooling2d[0][0]']
conv2d_1 (Conv2D) (None, 10, 65, 256) 590080 ['dropout_4[0][0]']
max_pooling2d_1 (MaxPooling2D) (None, 5, 33, 256) 0 ['conv2d_1[0][0]']
dropout_5 (Dropout) (None, 5, 33, 256) 0 ['max_pooling2d_1[0][0]']
conv2d_2 (Conv2D) (None, 5, 33, 256) 590080 ['dropout_5[0][0]']
features_data (InputLayer) [(None, 56)] 0 []
max_pooling2d_2 (MaxPooling2D) (None, 3, 17, 256) 0 ['conv2d_2[0][0]']
dense (Dense) (None, 256) 14592 ['features_data[0][0]']
dropout_6 (Dropout) (None, 3, 17, 256) 0 ['max_pooling2d_2[0][0]']
dropout (Dropout) (None, 256) 0 ['dense[0][0]']
conv2d_3 (Conv2D) (None, 3, 17, 512) 2097664 ['dropout_6[0][0]']
dense_1 (Dense) (None, 256) 65792 ['dropout[0][0]']
max_pooling2d_3 (MaxPooling2D) (None, 2, 9, 512) 0 ['conv2d_3[0][0]']
dropout_1 (Dropout) (None, 256) 0 ['dense_1[0][0]']
flatten (Flatten) (None, 9216) 0 ['max_pooling2d_3[0][0]']
dense_2 (Dense) (None, 128) 32896 ['dropout_1[0][0]']
dense_5 (Dense) (None, 128) 1179776 ['flatten[0][0]']
dropout_2 (Dropout) (None, 128) 0 ['dense_2[0][0]']
dropout_7 (Dropout) (None, 128) 0 ['dense_5[0][0]']
dense_3 (Dense) (None, 128) 16512 ['dropout_2[0][0]']
dense_6 (Dense) (None, 128) 16512 ['dropout_7[0][0]']
dropout_3 (Dropout) (None, 128) 0 ['dense_3[0][0]']
dropout_8 (Dropout) (None, 128) 0 ['dense_6[0][0]']
concatenate (Concatenate) (None, 256) 0 ['dropout_3[0][0]',
'dropout_8[0][0]']
dense_8 (Dense) (None, 64) 16448 ['concatenate[0][0]']
dense_9 (Dense) (None, 10) 650 ['dense_8[0][0]']
==================================================================================================
Total params: 4,623,562
Trainable params: 4,623,562
Non-trainable params: 0
__________________________________________________________________________________________________
- Training MMNN model:
Epoch 1/100
63/63 [==============================] - 9s 112ms/step - loss: 2.1448 - accuracy: 0.2144 - recall: 0.0286 - precision: 0.5055 - AUROC: 0.6765 - AUPRC: 0.2093 - f1_score: 0.0541 - balanced_accuracy: 0.5127 - specificity: 0.9969 - miss_rate: 0.9714 - fall_out: 0.0031 - mcc: 0.1019 - val_loss: 1.7215 - val_accuracy: 0.3881 - val_recall: 0.1247 - val_precision: 0.8006 - val_AUROC: 0.8309 - val_AUPRC: 0.4128 - val_f1_score: 0.2158 - val_balanced_accuracy: 0.5606 - val_specificity: 0.9966 - val_miss_rate: 0.8753 - val_fall_out: 0.0034 - val_mcc: 0.2937
Epoch 2/100
63/63 [==============================] - 7s 106ms/step - loss: 1.7232 - accuracy: 0.3719 - recall: 0.1413 - precision: 0.6032 - AUROC: 0.8280 - AUPRC: 0.3803 - f1_score: 0.2289 - balanced_accuracy: 0.5655 - specificity: 0.9897 - miss_rate: 0.8587 - fall_out: 0.0103 - mcc: 0.2598 - val_loss: 1.4400 - val_accuracy: 0.4972 - val_recall: 0.2344 - val_precision: 0.7813 - val_AUROC: 0.8844 - val_AUPRC: 0.5340 - val_f1_score: 0.3606 - val_balanced_accuracy: 0.6135 - val_specificity: 0.9927 - val_miss_rate: 0.7656 - val_fall_out: 0.0073 - val_mcc: 0.3994
Epoch 3/100
63/63 [==============================] - 7s 106ms/step - loss: 1.4979 - accuracy: 0.4515 - recall: 0.2249 - precision: 0.6588 - AUROC: 0.8739 - AUPRC: 0.4827 - f1_score: 0.3354 - balanced_accuracy: 0.6060 - specificity: 0.9871 - miss_rate: 0.7751 - fall_out: 0.0129 - mcc: 0.3502 - val_loss: 1.2346 - val_accuracy: 0.5739 - val_recall: 0.3235 - val_precision: 0.7600 - val_AUROC: 0.9170 - val_AUPRC: 0.6284 - val_f1_score: 0.4538 - val_balanced_accuracy: 0.6561 - val_specificity: 0.9886 - val_miss_rate: 0.6765 - val_fall_out: 0.0114 - val_mcc: 0.4639
Epoch 4/100
63/63 [==============================] - 7s 106ms/step - loss: 1.3256 - accuracy: 0.5317 - recall: 0.3148 - precision: 0.7065 - AUROC: 0.9026 - AUPRC: 0.5715 - f1_score: 0.4355 - balanced_accuracy: 0.6501 - specificity: 0.9855 - miss_rate: 0.6852 - fall_out: 0.0145 - mcc: 0.4365 - val_loss: 1.0755 - val_accuracy: 0.6254 - val_recall: 0.4046 - val_precision: 0.7914 - val_AUROC: 0.9364 - val_AUPRC: 0.6992 - val_f1_score: 0.5355 - val_balanced_accuracy: 0.6964 - val_specificity: 0.9881 - val_miss_rate: 0.5954 - val_fall_out: 0.0119 - val_mcc: 0.5350
Epoch 5/100
63/63 [==============================] - 7s 105ms/step - loss: 1.2215 - accuracy: 0.5698 - recall: 0.3780 - precision: 0.7246 - AUROC: 0.9169 - AUPRC: 0.6215 - f1_score: 0.4968 - balanced_accuracy: 0.6810 - specificity: 0.9840 - miss_rate: 0.6220 - fall_out: 0.0160 - mcc: 0.4884 - val_loss: 0.9973 - val_accuracy: 0.6775 - val_recall: 0.4457 - val_precision: 0.8452 - val_AUROC: 0.9474 - val_AUPRC: 0.7498 - val_f1_score: 0.5836 - val_balanced_accuracy: 0.7183 - val_specificity: 0.9909 - val_miss_rate: 0.5543 - val_fall_out: 0.0091 - val_mcc: 0.5861
Epoch 6/100
63/63 [==============================] - 7s 105ms/step - loss: 1.1181 - accuracy: 0.6100 - recall: 0.4330 - precision: 0.7583 - AUROC: 0.9297 - AUPRC: 0.6729 - f1_score: 0.5512 - balanced_accuracy: 0.7088 - specificity: 0.9847 - miss_rate: 0.5670 - fall_out: 0.0153 - mcc: 0.5400 - val_loss: 0.8836 - val_accuracy: 0.7041 - val_recall: 0.5633 - val_precision: 0.8321 - val_AUROC: 0.9554 - val_AUPRC: 0.7859 - val_f1_score: 0.6718 - val_balanced_accuracy: 0.7754 - val_specificity: 0.9874 - val_miss_rate: 0.4367 - val_fall_out: 0.0126 - val_mcc: 0.6576
Epoch 7/100
63/63 [==============================] - 7s 105ms/step - loss: 1.0401 - accuracy: 0.6439 - recall: 0.4959 - precision: 0.7763 - AUROC: 0.9384 - AUPRC: 0.7109 - f1_score: 0.6052 - balanced_accuracy: 0.7400 - specificity: 0.9841 - miss_rate: 0.5041 - fall_out: 0.0159 - mcc: 0.5889 - val_loss: 0.8413 - val_accuracy: 0.7151 - val_recall: 0.5959 - val_precision: 0.8316 - val_AUROC: 0.9600 - val_AUPRC: 0.8001 - val_f1_score: 0.6943 - val_balanced_accuracy: 0.7912 - val_specificity: 0.9866 - val_miss_rate: 0.4041 - val_fall_out: 0.0134 - val_mcc: 0.6775
Epoch 8/100
63/63 [==============================] - 7s 105ms/step - loss: 0.9783 - accuracy: 0.6642 - recall: 0.5318 - precision: 0.7808 - AUROC: 0.9451 - AUPRC: 0.7358 - f1_score: 0.6327 - balanced_accuracy: 0.7576 - specificity: 0.9834 - miss_rate: 0.4682 - fall_out: 0.0166 - mcc: 0.6135 - val_loss: 0.8078 - val_accuracy: 0.7291 - val_recall: 0.6169 - val_precision: 0.8319 - val_AUROC: 0.9628 - val_AUPRC: 0.8134 - val_f1_score: 0.7085 - val_balanced_accuracy: 0.8015 - val_specificity: 0.9861 - val_miss_rate: 0.3831 - val_fall_out: 0.0139 - val_mcc: 0.6905
Epoch 9/100
63/63 [==============================] - 7s 105ms/step - loss: 0.9214 - accuracy: 0.6881 - recall: 0.5650 - precision: 0.7900 - AUROC: 0.9510 - AUPRC: 0.7627 - f1_score: 0.6588 - balanced_accuracy: 0.7742 - specificity: 0.9833 - miss_rate: 0.4350 - fall_out: 0.0167 - mcc: 0.6384 - val_loss: 0.7694 - val_accuracy: 0.7486 - val_recall: 0.6355 - val_precision: 0.8454 - val_AUROC: 0.9657 - val_AUPRC: 0.8282 - val_f1_score: 0.7256 - val_balanced_accuracy: 0.8113 - val_specificity: 0.9871 - val_miss_rate: 0.3645 - val_fall_out: 0.0129 - val_mcc: 0.7084
Epoch 10/100
63/63 [==============================] - 7s 106ms/step - loss: 0.8599 - accuracy: 0.7118 - recall: 0.6046 - precision: 0.8129 - AUROC: 0.9571 - AUPRC: 0.7875 - f1_score: 0.6934 - balanced_accuracy: 0.7946 - specificity: 0.9845 - miss_rate: 0.3954 - fall_out: 0.0155 - mcc: 0.6736 - val_loss: 0.7020 - val_accuracy: 0.7666 - val_recall: 0.6940 - val_precision: 0.8545 - val_AUROC: 0.9712 - val_AUPRC: 0.8520 - val_f1_score: 0.7660 - val_balanced_accuracy: 0.8405 - val_specificity: 0.9869 - val_miss_rate: 0.3060 - val_fall_out: 0.0131 - val_mcc: 0.7478
Epoch 11/100
63/63 [==============================] - 7s 105ms/step - loss: 0.8103 - accuracy: 0.7296 - recall: 0.6315 - precision: 0.8138 - AUROC: 0.9616 - AUPRC: 0.8080 - f1_score: 0.7111 - balanced_accuracy: 0.8077 - specificity: 0.9839 - miss_rate: 0.3685 - fall_out: 0.0161 - mcc: 0.6901 - val_loss: 0.6769 - val_accuracy: 0.7817 - val_recall: 0.6960 - val_precision: 0.8634 - val_AUROC: 0.9728 - val_AUPRC: 0.8637 - val_f1_score: 0.7707 - val_balanced_accuracy: 0.8419 - val_specificity: 0.9878 - val_miss_rate: 0.3040 - val_fall_out: 0.0122 - val_mcc: 0.7535
Epoch 12/100
63/63 [==============================] - 7s 105ms/step - loss: 0.7823 - accuracy: 0.7390 - recall: 0.6463 - precision: 0.8276 - AUROC: 0.9638 - AUPRC: 0.8177 - f1_score: 0.7258 - balanced_accuracy: 0.8157 - specificity: 0.9850 - miss_rate: 0.3537 - fall_out: 0.0150 - mcc: 0.7059 - val_loss: 0.6417 - val_accuracy: 0.7857 - val_recall: 0.7266 - val_precision: 0.8500 - val_AUROC: 0.9751 - val_AUPRC: 0.8719 - val_f1_score: 0.7835 - val_balanced_accuracy: 0.8562 - val_specificity: 0.9858 - val_miss_rate: 0.2734 - val_fall_out: 0.0142 - val_mcc: 0.7643
Epoch 13/100
63/63 [==============================] - 7s 106ms/step - loss: 0.7250 - accuracy: 0.7616 - recall: 0.6775 - precision: 0.8287 - AUROC: 0.9683 - AUPRC: 0.8381 - f1_score: 0.7455 - balanced_accuracy: 0.8310 - specificity: 0.9844 - miss_rate: 0.3225 - fall_out: 0.0156 - mcc: 0.7248 - val_loss: 0.6169 - val_accuracy: 0.7872 - val_recall: 0.7276 - val_precision: 0.8623 - val_AUROC: 0.9777 - val_AUPRC: 0.8817 - val_f1_score: 0.7892 - val_balanced_accuracy: 0.8573 - val_specificity: 0.9871 - val_miss_rate: 0.2724 - val_fall_out: 0.0129 - val_mcc: 0.7714
Epoch 14/100
63/63 [==============================] - 7s 105ms/step - loss: 0.7064 - accuracy: 0.7683 - recall: 0.6888 - precision: 0.8367 - AUROC: 0.9701 - AUPRC: 0.8433 - f1_score: 0.7556 - balanced_accuracy: 0.8369 - specificity: 0.9851 - miss_rate: 0.3112 - fall_out: 0.0149 - mcc: 0.7355 - val_loss: 0.6031 - val_accuracy: 0.8022 - val_recall: 0.7406 - val_precision: 0.8639 - val_AUROC: 0.9776 - val_AUPRC: 0.8846 - val_f1_score: 0.7975 - val_balanced_accuracy: 0.8638 - val_specificity: 0.9870 - val_miss_rate: 0.2594 - val_fall_out: 0.0130 - val_mcc: 0.7797
Epoch 15/100
63/63 [==============================] - 7s 105ms/step - loss: 0.6728 - accuracy: 0.7796 - recall: 0.7093 - precision: 0.8416 - AUROC: 0.9724 - AUPRC: 0.8577 - f1_score: 0.7698 - balanced_accuracy: 0.8472 - specificity: 0.9852 - miss_rate: 0.2907 - fall_out: 0.0148 - mcc: 0.7499 - val_loss: 0.5696 - val_accuracy: 0.8052 - val_recall: 0.7556 - val_precision: 0.8692 - val_AUROC: 0.9803 - val_AUPRC: 0.8950 - val_f1_score: 0.8085 - val_balanced_accuracy: 0.8715 - val_specificity: 0.9874 - val_miss_rate: 0.2444 - val_fall_out: 0.0126 - val_mcc: 0.7912
Epoch 16/100
63/63 [==============================] - 7s 106ms/step - loss: 0.6258 - accuracy: 0.7937 - recall: 0.7286 - precision: 0.8541 - AUROC: 0.9759 - AUPRC: 0.8703 - f1_score: 0.7863 - balanced_accuracy: 0.8574 - specificity: 0.9862 - miss_rate: 0.2714 - fall_out: 0.0138 - mcc: 0.7676 - val_loss: 0.5427 - val_accuracy: 0.8222 - val_recall: 0.7727 - val_precision: 0.8718 - val_AUROC: 0.9816 - val_AUPRC: 0.9033 - val_f1_score: 0.8192 - val_balanced_accuracy: 0.8800 - val_specificity: 0.9874 - val_miss_rate: 0.2273 - val_fall_out: 0.0126 - val_mcc: 0.8022
Epoch 17/100
63/63 [==============================] - 7s 105ms/step - loss: 0.6206 - accuracy: 0.7935 - recall: 0.7328 - precision: 0.8507 - AUROC: 0.9759 - AUPRC: 0.8730 - f1_score: 0.7874 - balanced_accuracy: 0.8593 - specificity: 0.9857 - miss_rate: 0.2672 - fall_out: 0.0143 - mcc: 0.7683 - val_loss: 0.5269 - val_accuracy: 0.8177 - val_recall: 0.7692 - val_precision: 0.8722 - val_AUROC: 0.9830 - val_AUPRC: 0.9093 - val_f1_score: 0.8175 - val_balanced_accuracy: 0.8783 - val_specificity: 0.9875 - val_miss_rate: 0.2308 - val_fall_out: 0.0125 - val_mcc: 0.8005
Epoch 18/100
63/63 [==============================] - 7s 105ms/step - loss: 0.5601 - accuracy: 0.8117 - recall: 0.7609 - precision: 0.8634 - AUROC: 0.9803 - AUPRC: 0.8940 - f1_score: 0.8089 - balanced_accuracy: 0.8738 - specificity: 0.9866 - miss_rate: 0.2391 - fall_out: 0.0134 - mcc: 0.7911 - val_loss: 0.5083 - val_accuracy: 0.8192 - val_recall: 0.7837 - val_precision: 0.8699 - val_AUROC: 0.9840 - val_AUPRC: 0.9127 - val_f1_score: 0.8246 - val_balanced_accuracy: 0.8853 - val_specificity: 0.9870 - val_miss_rate: 0.2163 - val_fall_out: 0.0130 - val_mcc: 0.8075
Epoch 19/100
63/63 [==============================] - 7s 106ms/step - loss: 0.5256 - accuracy: 0.8270 - recall: 0.7779 - precision: 0.8750 - AUROC: 0.9818 - AUPRC: 0.9045 - f1_score: 0.8236 - balanced_accuracy: 0.8828 - specificity: 0.9877 - miss_rate: 0.2221 - fall_out: 0.0123 - mcc: 0.8070 - val_loss: 0.4961 - val_accuracy: 0.8388 - val_recall: 0.7967 - val_precision: 0.8834 - val_AUROC: 0.9835 - val_AUPRC: 0.9167 - val_f1_score: 0.8378 - val_balanced_accuracy: 0.8925 - val_specificity: 0.9883 - val_miss_rate: 0.2033 - val_fall_out: 0.0117 - val_mcc: 0.8222
Epoch 20/100
63/63 [==============================] - 7s 106ms/step - loss: 0.5172 - accuracy: 0.8290 - recall: 0.7843 - precision: 0.8735 - AUROC: 0.9826 - AUPRC: 0.9077 - f1_score: 0.8265 - balanced_accuracy: 0.8858 - specificity: 0.9874 - miss_rate: 0.2157 - fall_out: 0.0126 - mcc: 0.8098 - val_loss: 0.4839 - val_accuracy: 0.8468 - val_recall: 0.8082 - val_precision: 0.8829 - val_AUROC: 0.9839 - val_AUPRC: 0.9189 - val_f1_score: 0.8439 - val_balanced_accuracy: 0.8982 - val_specificity: 0.9881 - val_miss_rate: 0.1918 - val_fall_out: 0.0119 - val_mcc: 0.8284
Epoch 21/100
63/63 [==============================] - 7s 105ms/step - loss: 0.4804 - accuracy: 0.8424 - recall: 0.8020 - precision: 0.8810 - AUROC: 0.9849 - AUPRC: 0.9166 - f1_score: 0.8396 - balanced_accuracy: 0.8950 - specificity: 0.9880 - miss_rate: 0.1980 - fall_out: 0.0120 - mcc: 0.8238 - val_loss: 0.4659 - val_accuracy: 0.8478 - val_recall: 0.8197 - val_precision: 0.8873 - val_AUROC: 0.9850 - val_AUPRC: 0.9235 - val_f1_score: 0.8522 - val_balanced_accuracy: 0.9041 - val_specificity: 0.9884 - val_miss_rate: 0.1803 - val_fall_out: 0.0116 - val_mcc: 0.8373
Epoch 22/100
63/63 [==============================] - 7s 105ms/step - loss: 0.4650 - accuracy: 0.8492 - recall: 0.8121 - precision: 0.8870 - AUROC: 0.9850 - AUPRC: 0.9214 - f1_score: 0.8479 - balanced_accuracy: 0.9003 - specificity: 0.9885 - miss_rate: 0.1879 - fall_out: 0.0115 - mcc: 0.8328 - val_loss: 0.4541 - val_accuracy: 0.8488 - val_recall: 0.8177 - val_precision: 0.8865 - val_AUROC: 0.9862 - val_AUPRC: 0.9286 - val_f1_score: 0.8507 - val_balanced_accuracy: 0.9030 - val_specificity: 0.9884 - val_miss_rate: 0.1823 - val_fall_out: 0.0116 - val_mcc: 0.8357
Epoch 23/100
63/63 [==============================] - 7s 105ms/step - loss: 0.4197 - accuracy: 0.8622 - recall: 0.8303 - precision: 0.8947 - AUROC: 0.9878 - AUPRC: 0.9334 - f1_score: 0.8613 - balanced_accuracy: 0.9097 - specificity: 0.9891 - miss_rate: 0.1697 - fall_out: 0.0109 - mcc: 0.8473 - val_loss: 0.4373 - val_accuracy: 0.8578 - val_recall: 0.8267 - val_precision: 0.8924 - val_AUROC: 0.9867 - val_AUPRC: 0.9319 - val_f1_score: 0.8583 - val_balanced_accuracy: 0.9078 - val_specificity: 0.9889 - val_miss_rate: 0.1733 - val_fall_out: 0.0111 - val_mcc: 0.8440
Epoch 24/100
63/63 [==============================] - 7s 105ms/step - loss: 0.4182 - accuracy: 0.8617 - recall: 0.8322 - precision: 0.8936 - AUROC: 0.9878 - AUPRC: 0.9327 - f1_score: 0.8618 - balanced_accuracy: 0.9106 - specificity: 0.9890 - miss_rate: 0.1678 - fall_out: 0.0110 - mcc: 0.8477 - val_loss: 0.4329 - val_accuracy: 0.8568 - val_recall: 0.8322 - val_precision: 0.8869 - val_AUROC: 0.9868 - val_AUPRC: 0.9331 - val_f1_score: 0.8587 - val_balanced_accuracy: 0.9102 - val_specificity: 0.9882 - val_miss_rate: 0.1678 - val_fall_out: 0.0118 - val_mcc: 0.8441
Epoch 25/100
63/63 [==============================] - 7s 105ms/step - loss: 0.3832 - accuracy: 0.8707 - recall: 0.8426 - precision: 0.9002 - AUROC: 0.9897 - AUPRC: 0.9421 - f1_score: 0.8704 - balanced_accuracy: 0.9161 - specificity: 0.9896 - miss_rate: 0.1574 - fall_out: 0.0104 - mcc: 0.8571 - val_loss: 0.4278 - val_accuracy: 0.8618 - val_recall: 0.8403 - val_precision: 0.8874 - val_AUROC: 0.9866 - val_AUPRC: 0.9340 - val_f1_score: 0.8632 - val_balanced_accuracy: 0.9142 - val_specificity: 0.9881 - val_miss_rate: 0.1597 - val_fall_out: 0.0119 - val_mcc: 0.8488
Epoch 26/100
63/63 [==============================] - 7s 106ms/step - loss: 0.3899 - accuracy: 0.8722 - recall: 0.8468 - precision: 0.9000 - AUROC: 0.9890 - AUPRC: 0.9397 - f1_score: 0.8726 - balanced_accuracy: 0.9182 - specificity: 0.9895 - miss_rate: 0.1532 - fall_out: 0.0105 - mcc: 0.8594 - val_loss: 0.4099 - val_accuracy: 0.8663 - val_recall: 0.8393 - val_precision: 0.8991 - val_AUROC: 0.9890 - val_AUPRC: 0.9370 - val_f1_score: 0.8682 - val_balanced_accuracy: 0.9144 - val_specificity: 0.9895 - val_miss_rate: 0.1607 - val_fall_out: 0.0105 - val_mcc: 0.8547
Epoch 27/100
63/63 [==============================] - 7s 105ms/step - loss: 0.3710 - accuracy: 0.8768 - recall: 0.8484 - precision: 0.9045 - AUROC: 0.9903 - AUPRC: 0.9449 - f1_score: 0.8756 - balanced_accuracy: 0.9192 - specificity: 0.9900 - miss_rate: 0.1516 - fall_out: 0.0100 - mcc: 0.8628 - val_loss: 0.4228 - val_accuracy: 0.8578 - val_recall: 0.8378 - val_precision: 0.8861 - val_AUROC: 0.9871 - val_AUPRC: 0.9331 - val_f1_score: 0.8613 - val_balanced_accuracy: 0.9129 - val_specificity: 0.9880 - val_miss_rate: 0.1622 - val_fall_out: 0.0120 - val_mcc: 0.8467
Epoch 28/100
63/63 [==============================] - 7s 105ms/step - loss: 0.3175 - accuracy: 0.8980 - recall: 0.8770 - precision: 0.9190 - AUROC: 0.9919 - AUPRC: 0.9568 - f1_score: 0.8975 - balanced_accuracy: 0.9342 - specificity: 0.9914 - miss_rate: 0.1230 - fall_out: 0.0086 - mcc: 0.8867 - val_loss: 0.4212 - val_accuracy: 0.8753 - val_recall: 0.8508 - val_precision: 0.8919 - val_AUROC: 0.9864 - val_AUPRC: 0.9370 - val_f1_score: 0.8708 - val_balanced_accuracy: 0.9197 - val_specificity: 0.9885 - val_miss_rate: 0.1492 - val_fall_out: 0.0115 - val_mcc: 0.8572
63/63 [==============================] - 4s 62ms/step - loss: 0.1156 - accuracy: 0.9678 - recall: 0.9589 - precision: 0.9765 - AUROC: 0.9993 - AUPRC: 0.9947 - f1_score: 0.9676 - balanced_accuracy: 0.9782 - specificity: 0.9974 - miss_rate: 0.0411 - fall_out: 0.0026 - mcc: 0.9641
16/16 [==============================] - 3s 181ms/step - loss: 0.4212 - accuracy: 0.8753 - recall: 0.8508 - precision: 0.8919 - AUROC: 0.9864 - AUPRC: 0.9370 - f1_score: 0.8708 - balanced_accuracy: 0.9197 - specificity: 0.9885 - miss_rate: 0.1492 - fall_out: 0.0115 - mcc: 0.8572
-- HOLDOUT 7 --
-- 6 Uncorrelated features: [Pearson+Spearman]
['mfcc10_var', 'mfcc17_mean', 'mfcc11_var', 'mfcc16_mean', 'tempo', 'mfcc19_mean']
-- Actually uncorrelated features: [confirmed via MIC]
[]
-- 1 Correlated features: [Pearson]
['spectral_centroid_mean']
- Removing uncorrelated/correlated features: ['spectral_centroid_mean']
- Building Fixed MLP:
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
features_data (InputLayer) [(None, 56)] 0
dense (Dense) (None, 256) 14592
dropout (Dropout) (None, 256) 0
dense_1 (Dense) (None, 256) 65792
dropout_1 (Dropout) (None, 256) 0
dense_2 (Dense) (None, 128) 32896
dropout_2 (Dropout) (None, 128) 0
dense_3 (Dense) (None, 128) 16512
dropout_3 (Dropout) (None, 128) 0
dense_4 (Dense) (None, 10) 1290
=================================================================
Total params: 131,082
Trainable params: 131,082
Non-trainable params: 0
_________________________________________________________________
- Building Fixed CNN:
Model: "model_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
mfccs_data (InputLayer) [(None, 20, 130, 1)] 0
conv2d (Conv2D) (None, 20, 130, 256) 2560
max_pooling2d (MaxPooling2D (None, 10, 65, 256) 0
)
dropout_4 (Dropout) (None, 10, 65, 256) 0
conv2d_1 (Conv2D) (None, 10, 65, 256) 590080
max_pooling2d_1 (MaxPooling (None, 5, 33, 256) 0
2D)
dropout_5 (Dropout) (None, 5, 33, 256) 0
conv2d_2 (Conv2D) (None, 5, 33, 256) 590080
max_pooling2d_2 (MaxPooling (None, 3, 17, 256) 0
2D)
dropout_6 (Dropout) (None, 3, 17, 256) 0
conv2d_3 (Conv2D) (None, 3, 17, 512) 2097664
max_pooling2d_3 (MaxPooling (None, 2, 9, 512) 0
2D)
flatten (Flatten) (None, 9216) 0
dense_5 (Dense) (None, 128) 1179776
dropout_7 (Dropout) (None, 128) 0
dense_6 (Dense) (None, 128) 16512
dropout_8 (Dropout) (None, 128) 0
dense_7 (Dense) (None, 10) 1290
=================================================================
Total params: 4,477,962
Trainable params: 4,477,962
Non-trainable params: 0
_________________________________________________________________
- Building MMNN:
Model: "model_2"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
mfccs_data (InputLayer) [(None, 20, 130, 1) 0 []
]
conv2d (Conv2D) (None, 20, 130, 256 2560 ['mfccs_data[0][0]']
)
max_pooling2d (MaxPooling2D) (None, 10, 65, 256) 0 ['conv2d[0][0]']
dropout_4 (Dropout) (None, 10, 65, 256) 0 ['max_pooling2d[0][0]']
conv2d_1 (Conv2D) (None, 10, 65, 256) 590080 ['dropout_4[0][0]']
max_pooling2d_1 (MaxPooling2D) (None, 5, 33, 256) 0 ['conv2d_1[0][0]']
dropout_5 (Dropout) (None, 5, 33, 256) 0 ['max_pooling2d_1[0][0]']
conv2d_2 (Conv2D) (None, 5, 33, 256) 590080 ['dropout_5[0][0]']
features_data (InputLayer) [(None, 56)] 0 []
max_pooling2d_2 (MaxPooling2D) (None, 3, 17, 256) 0 ['conv2d_2[0][0]']
dense (Dense) (None, 256) 14592 ['features_data[0][0]']
dropout_6 (Dropout) (None, 3, 17, 256) 0 ['max_pooling2d_2[0][0]']
dropout (Dropout) (None, 256) 0 ['dense[0][0]']
conv2d_3 (Conv2D) (None, 3, 17, 512) 2097664 ['dropout_6[0][0]']
dense_1 (Dense) (None, 256) 65792 ['dropout[0][0]']
max_pooling2d_3 (MaxPooling2D) (None, 2, 9, 512) 0 ['conv2d_3[0][0]']
dropout_1 (Dropout) (None, 256) 0 ['dense_1[0][0]']
flatten (Flatten) (None, 9216) 0 ['max_pooling2d_3[0][0]']
dense_2 (Dense) (None, 128) 32896 ['dropout_1[0][0]']
dense_5 (Dense) (None, 128) 1179776 ['flatten[0][0]']
dropout_2 (Dropout) (None, 128) 0 ['dense_2[0][0]']
dropout_7 (Dropout) (None, 128) 0 ['dense_5[0][0]']
dense_3 (Dense) (None, 128) 16512 ['dropout_2[0][0]']
dense_6 (Dense) (None, 128) 16512 ['dropout_7[0][0]']
dropout_3 (Dropout) (None, 128) 0 ['dense_3[0][0]']
dropout_8 (Dropout) (None, 128) 0 ['dense_6[0][0]']
concatenate (Concatenate) (None, 256) 0 ['dropout_3[0][0]',
'dropout_8[0][0]']
dense_8 (Dense) (None, 64) 16448 ['concatenate[0][0]']
dense_9 (Dense) (None, 10) 650 ['dense_8[0][0]']
==================================================================================================
Total params: 4,623,562
Trainable params: 4,623,562
Non-trainable params: 0
__________________________________________________________________________________________________
- Training MMNN model:
Epoch 1/100
63/63 [==============================] - 9s 112ms/step - loss: 2.1860 - accuracy: 0.2083 - recall: 0.0245 - precision: 0.4579 - AUROC: 0.6633 - AUPRC: 0.1957 - f1_score: 0.0466 - balanced_accuracy: 0.5107 - specificity: 0.9968 - miss_rate: 0.9755 - fall_out: 0.0032 - mcc: 0.0876 - val_loss: 1.6833 - val_accuracy: 0.3921 - val_recall: 0.1652 - val_precision: 0.7820 - val_AUROC: 0.8414 - val_AUPRC: 0.4345 - val_f1_score: 0.2728 - val_balanced_accuracy: 0.5801 - val_specificity: 0.9949 - val_miss_rate: 0.8348 - val_fall_out: 0.0051 - val_mcc: 0.3340
Epoch 2/100
63/63 [==============================] - 7s 106ms/step - loss: 1.6841 - accuracy: 0.3818 - recall: 0.1673 - precision: 0.6168 - AUROC: 0.8369 - AUPRC: 0.4003 - f1_score: 0.2633 - balanced_accuracy: 0.5779 - specificity: 0.9884 - miss_rate: 0.8327 - fall_out: 0.0116 - mcc: 0.2877 - val_loss: 1.3705 - val_accuracy: 0.4892 - val_recall: 0.2704 - val_precision: 0.7469 - val_AUROC: 0.9049 - val_AUPRC: 0.5736 - val_f1_score: 0.3971 - val_balanced_accuracy: 0.6301 - val_specificity: 0.9898 - val_miss_rate: 0.7296 - val_fall_out: 0.0102 - val_mcc: 0.4179
Epoch 3/100
63/63 [==============================] - 7s 107ms/step - loss: 1.4745 - accuracy: 0.4692 - recall: 0.2472 - precision: 0.6821 - AUROC: 0.8788 - AUPRC: 0.4954 - f1_score: 0.3629 - balanced_accuracy: 0.6172 - specificity: 0.9872 - miss_rate: 0.7528 - fall_out: 0.0128 - mcc: 0.3763 - val_loss: 1.1914 - val_accuracy: 0.5749 - val_recall: 0.3365 - val_precision: 0.7953 - val_AUROC: 0.9265 - val_AUPRC: 0.6499 - val_f1_score: 0.4729 - val_balanced_accuracy: 0.6634 - val_specificity: 0.9904 - val_miss_rate: 0.6635 - val_fall_out: 0.0096 - val_mcc: 0.4871
Epoch 4/100
63/63 [==============================] - 7s 106ms/step - loss: 1.3080 - accuracy: 0.5352 - recall: 0.3236 - precision: 0.7212 - AUROC: 0.9045 - AUPRC: 0.5812 - f1_score: 0.4468 - balanced_accuracy: 0.6549 - specificity: 0.9861 - miss_rate: 0.6764 - fall_out: 0.0139 - mcc: 0.4488 - val_loss: 1.0600 - val_accuracy: 0.6630 - val_recall: 0.4076 - val_precision: 0.8506 - val_AUROC: 0.9421 - val_AUPRC: 0.7194 - val_f1_score: 0.5511 - val_balanced_accuracy: 0.6998 - val_specificity: 0.9920 - val_miss_rate: 0.5924 - val_fall_out: 0.0080 - val_mcc: 0.5613
Epoch 5/100
63/63 [==============================] - 7s 106ms/step - loss: 1.2149 - accuracy: 0.5675 - recall: 0.3717 - precision: 0.7405 - AUROC: 0.9177 - AUPRC: 0.6237 - f1_score: 0.4950 - balanced_accuracy: 0.6786 - specificity: 0.9855 - miss_rate: 0.6283 - fall_out: 0.0145 - mcc: 0.4908 - val_loss: 0.9379 - val_accuracy: 0.6940 - val_recall: 0.4857 - val_precision: 0.8554 - val_AUROC: 0.9536 - val_AUPRC: 0.7692 - val_f1_score: 0.6196 - val_balanced_accuracy: 0.7383 - val_specificity: 0.9909 - val_miss_rate: 0.5143 - val_fall_out: 0.0091 - val_mcc: 0.6178
Epoch 6/100
63/63 [==============================] - 7s 107ms/step - loss: 1.1159 - accuracy: 0.6047 - recall: 0.4376 - precision: 0.7558 - AUROC: 0.9302 - AUPRC: 0.6722 - f1_score: 0.5543 - balanced_accuracy: 0.7110 - specificity: 0.9843 - miss_rate: 0.5624 - fall_out: 0.0157 - mcc: 0.5419 - val_loss: 0.8918 - val_accuracy: 0.6970 - val_recall: 0.5203 - val_precision: 0.8427 - val_AUROC: 0.9575 - val_AUPRC: 0.7859 - val_f1_score: 0.6433 - val_balanced_accuracy: 0.7547 - val_specificity: 0.9892 - val_miss_rate: 0.4797 - val_fall_out: 0.0108 - val_mcc: 0.6350
Epoch 7/100
63/63 [==============================] - 7s 106ms/step - loss: 1.0251 - accuracy: 0.6487 - recall: 0.4951 - precision: 0.7682 - AUROC: 0.9400 - AUPRC: 0.7153 - f1_score: 0.6021 - balanced_accuracy: 0.7393 - specificity: 0.9834 - miss_rate: 0.5049 - fall_out: 0.0166 - mcc: 0.5846 - val_loss: 0.8576 - val_accuracy: 0.7091 - val_recall: 0.5543 - val_precision: 0.8509 - val_AUROC: 0.9609 - val_AUPRC: 0.8001 - val_f1_score: 0.6713 - val_balanced_accuracy: 0.7718 - val_specificity: 0.9892 - val_miss_rate: 0.4457 - val_fall_out: 0.0108 - val_mcc: 0.6607
Epoch 8/100
63/63 [==============================] - 7s 106ms/step - loss: 0.9735 - accuracy: 0.6705 - recall: 0.5307 - precision: 0.7861 - AUROC: 0.9462 - AUPRC: 0.7384 - f1_score: 0.6336 - balanced_accuracy: 0.7573 - specificity: 0.9840 - miss_rate: 0.4693 - fall_out: 0.0160 - mcc: 0.6153 - val_loss: 0.8026 - val_accuracy: 0.7431 - val_recall: 0.6129 - val_precision: 0.8638 - val_AUROC: 0.9640 - val_AUPRC: 0.8233 - val_f1_score: 0.7170 - val_balanced_accuracy: 0.8011 - val_specificity: 0.9893 - val_miss_rate: 0.3871 - val_fall_out: 0.0107 - val_mcc: 0.7036
Epoch 9/100
63/63 [==============================] - 7s 106ms/step - loss: 0.9288 - accuracy: 0.6904 - recall: 0.5636 - precision: 0.7987 - AUROC: 0.9506 - AUPRC: 0.7603 - f1_score: 0.6609 - balanced_accuracy: 0.7739 - specificity: 0.9842 - miss_rate: 0.4364 - fall_out: 0.0158 - mcc: 0.6418 - val_loss: 0.7319 - val_accuracy: 0.7646 - val_recall: 0.6460 - val_precision: 0.8606 - val_AUROC: 0.9695 - val_AUPRC: 0.8463 - val_f1_score: 0.7380 - val_balanced_accuracy: 0.8172 - val_specificity: 0.9884 - val_miss_rate: 0.3540 - val_fall_out: 0.0116 - val_mcc: 0.7222
Epoch 10/100
63/63 [==============================] - 7s 106ms/step - loss: 0.8779 - accuracy: 0.7039 - recall: 0.5901 - precision: 0.8001 - AUROC: 0.9559 - AUPRC: 0.7782 - f1_score: 0.6792 - balanced_accuracy: 0.7868 - specificity: 0.9836 - miss_rate: 0.4099 - fall_out: 0.0164 - mcc: 0.6585 - val_loss: 0.7012 - val_accuracy: 0.7747 - val_recall: 0.6685 - val_precision: 0.8697 - val_AUROC: 0.9714 - val_AUPRC: 0.8554 - val_f1_score: 0.7559 - val_balanced_accuracy: 0.8287 - val_specificity: 0.9889 - val_miss_rate: 0.3315 - val_fall_out: 0.0111 - val_mcc: 0.7403
Epoch 11/100
63/63 [==============================] - 7s 106ms/step - loss: 0.8444 - accuracy: 0.7148 - recall: 0.6103 - precision: 0.8087 - AUROC: 0.9582 - AUPRC: 0.7928 - f1_score: 0.6956 - balanced_accuracy: 0.7971 - specificity: 0.9840 - miss_rate: 0.3897 - fall_out: 0.0160 - mcc: 0.6749 - val_loss: 0.6825 - val_accuracy: 0.7787 - val_recall: 0.6800 - val_precision: 0.8716 - val_AUROC: 0.9731 - val_AUPRC: 0.8613 - val_f1_score: 0.7640 - val_balanced_accuracy: 0.8344 - val_specificity: 0.9889 - val_miss_rate: 0.3200 - val_fall_out: 0.0111 - val_mcc: 0.7482
Epoch 12/100
63/63 [==============================] - 7s 106ms/step - loss: 0.7850 - accuracy: 0.7387 - recall: 0.6414 - precision: 0.8232 - AUROC: 0.9640 - AUPRC: 0.8171 - f1_score: 0.7210 - balanced_accuracy: 0.8130 - specificity: 0.9847 - miss_rate: 0.3586 - fall_out: 0.0153 - mcc: 0.7007 - val_loss: 0.6594 - val_accuracy: 0.7847 - val_recall: 0.6850 - val_precision: 0.8691 - val_AUROC: 0.9752 - val_AUPRC: 0.8707 - val_f1_score: 0.7662 - val_balanced_accuracy: 0.8368 - val_specificity: 0.9885 - val_miss_rate: 0.3150 - val_fall_out: 0.0115 - val_mcc: 0.7499
Epoch 13/100
63/63 [==============================] - 7s 106ms/step - loss: 0.7463 - accuracy: 0.7514 - recall: 0.6623 - precision: 0.8312 - AUROC: 0.9669 - AUPRC: 0.8305 - f1_score: 0.7372 - balanced_accuracy: 0.8237 - specificity: 0.9851 - miss_rate: 0.3377 - fall_out: 0.0149 - mcc: 0.7172 - val_loss: 0.6287 - val_accuracy: 0.8022 - val_recall: 0.7246 - val_precision: 0.8649 - val_AUROC: 0.9762 - val_AUPRC: 0.8777 - val_f1_score: 0.7886 - val_balanced_accuracy: 0.8560 - val_specificity: 0.9874 - val_miss_rate: 0.2754 - val_fall_out: 0.0126 - val_mcc: 0.7710
Epoch 14/100
63/63 [==============================] - 7s 106ms/step - loss: 0.7245 - accuracy: 0.7534 - recall: 0.6715 - precision: 0.8339 - AUROC: 0.9690 - AUPRC: 0.8382 - f1_score: 0.7439 - balanced_accuracy: 0.8283 - specificity: 0.9851 - miss_rate: 0.3285 - fall_out: 0.0149 - mcc: 0.7239 - val_loss: 0.6041 - val_accuracy: 0.7972 - val_recall: 0.7251 - val_precision: 0.8728 - val_AUROC: 0.9780 - val_AUPRC: 0.8843 - val_f1_score: 0.7921 - val_balanced_accuracy: 0.8567 - val_specificity: 0.9883 - val_miss_rate: 0.2749 - val_fall_out: 0.0117 - val_mcc: 0.7754
Epoch 15/100
63/63 [==============================] - 7s 106ms/step - loss: 0.7061 - accuracy: 0.7678 - recall: 0.6855 - precision: 0.8374 - AUROC: 0.9700 - AUPRC: 0.8455 - f1_score: 0.7539 - balanced_accuracy: 0.8354 - specificity: 0.9852 - miss_rate: 0.3145 - fall_out: 0.0148 - mcc: 0.7339 - val_loss: 0.5988 - val_accuracy: 0.7882 - val_recall: 0.7291 - val_precision: 0.8724 - val_AUROC: 0.9789 - val_AUPRC: 0.8858 - val_f1_score: 0.7943 - val_balanced_accuracy: 0.8586 - val_specificity: 0.9881 - val_miss_rate: 0.2709 - val_fall_out: 0.0119 - val_mcc: 0.7775
Epoch 16/100
63/63 [==============================] - 7s 106ms/step - loss: 0.6571 - accuracy: 0.7811 - recall: 0.7083 - precision: 0.8474 - AUROC: 0.9732 - AUPRC: 0.8633 - f1_score: 0.7716 - balanced_accuracy: 0.8471 - specificity: 0.9858 - miss_rate: 0.2917 - fall_out: 0.0142 - mcc: 0.7524 - val_loss: 0.5518 - val_accuracy: 0.8167 - val_recall: 0.7666 - val_precision: 0.8794 - val_AUROC: 0.9811 - val_AUPRC: 0.9017 - val_f1_score: 0.8192 - val_balanced_accuracy: 0.8775 - val_specificity: 0.9883 - val_miss_rate: 0.2334 - val_fall_out: 0.0117 - val_mcc: 0.8029
Epoch 17/100
63/63 [==============================] - 7s 106ms/step - loss: 0.6215 - accuracy: 0.7893 - recall: 0.7268 - precision: 0.8536 - AUROC: 0.9761 - AUPRC: 0.8743 - f1_score: 0.7851 - balanced_accuracy: 0.8565 - specificity: 0.9862 - miss_rate: 0.2732 - fall_out: 0.0138 - mcc: 0.7664 - val_loss: 0.5409 - val_accuracy: 0.8287 - val_recall: 0.7707 - val_precision: 0.8774 - val_AUROC: 0.9818 - val_AUPRC: 0.9049 - val_f1_score: 0.8206 - val_balanced_accuracy: 0.8793 - val_specificity: 0.9880 - val_miss_rate: 0.2293 - val_fall_out: 0.0120 - val_mcc: 0.8041
Epoch 18/100
63/63 [==============================] - 7s 106ms/step - loss: 0.6057 - accuracy: 0.7930 - recall: 0.7372 - precision: 0.8556 - AUROC: 0.9775 - AUPRC: 0.8786 - f1_score: 0.7920 - balanced_accuracy: 0.8617 - specificity: 0.9862 - miss_rate: 0.2628 - fall_out: 0.0138 - mcc: 0.7734 - val_loss: 0.5078 - val_accuracy: 0.8383 - val_recall: 0.7932 - val_precision: 0.8879 - val_AUROC: 0.9835 - val_AUPRC: 0.9144 - val_f1_score: 0.8379 - val_balanced_accuracy: 0.8910 - val_specificity: 0.9889 - val_miss_rate: 0.2068 - val_fall_out: 0.0111 - val_mcc: 0.8226
Epoch 19/100
63/63 [==============================] - 7s 106ms/step - loss: 0.5832 - accuracy: 0.8104 - recall: 0.7529 - precision: 0.8604 - AUROC: 0.9788 - AUPRC: 0.8875 - f1_score: 0.8031 - balanced_accuracy: 0.8697 - specificity: 0.9864 - miss_rate: 0.2471 - fall_out: 0.0136 - mcc: 0.7849 - val_loss: 0.6236 - val_accuracy: 0.7982 - val_recall: 0.7316 - val_precision: 0.8696 - val_AUROC: 0.9758 - val_AUPRC: 0.8797 - val_f1_score: 0.7947 - val_balanced_accuracy: 0.8597 - val_specificity: 0.9878 - val_miss_rate: 0.2684 - val_fall_out: 0.0122 - val_mcc: 0.7775
Epoch 20/100
63/63 [==============================] - 7s 106ms/step - loss: 0.5652 - accuracy: 0.8124 - recall: 0.7544 - precision: 0.8638 - AUROC: 0.9802 - AUPRC: 0.8921 - f1_score: 0.8054 - balanced_accuracy: 0.8706 - specificity: 0.9868 - miss_rate: 0.2456 - fall_out: 0.0132 - mcc: 0.7876 - val_loss: 0.4749 - val_accuracy: 0.8463 - val_recall: 0.8117 - val_precision: 0.8882 - val_AUROC: 0.9844 - val_AUPRC: 0.9227 - val_f1_score: 0.8482 - val_balanced_accuracy: 0.9002 - val_specificity: 0.9886 - val_miss_rate: 0.1883 - val_fall_out: 0.0114 - val_mcc: 0.8333
Epoch 21/100
63/63 [==============================] - 7s 106ms/step - loss: 0.5509 - accuracy: 0.8136 - recall: 0.7647 - precision: 0.8658 - AUROC: 0.9810 - AUPRC: 0.8969 - f1_score: 0.8121 - balanced_accuracy: 0.8757 - specificity: 0.9868 - miss_rate: 0.2353 - fall_out: 0.0132 - mcc: 0.7945 - val_loss: 0.4564 - val_accuracy: 0.8533 - val_recall: 0.8092 - val_precision: 0.8953 - val_AUROC: 0.9853 - val_AUPRC: 0.9266 - val_f1_score: 0.8501 - val_balanced_accuracy: 0.8993 - val_specificity: 0.9895 - val_miss_rate: 0.1908 - val_fall_out: 0.0105 - val_mcc: 0.8357
Epoch 22/100
63/63 [==============================] - 7s 107ms/step - loss: 0.5133 - accuracy: 0.8288 - recall: 0.7788 - precision: 0.8725 - AUROC: 0.9832 - AUPRC: 0.9067 - f1_score: 0.8230 - balanced_accuracy: 0.8831 - specificity: 0.9873 - miss_rate: 0.2212 - fall_out: 0.0127 - mcc: 0.8061 - val_loss: 0.4375 - val_accuracy: 0.8628 - val_recall: 0.8307 - val_precision: 0.9056 - val_AUROC: 0.9872 - val_AUPRC: 0.9333 - val_f1_score: 0.8665 - val_balanced_accuracy: 0.9106 - val_specificity: 0.9904 - val_miss_rate: 0.1693 - val_fall_out: 0.0096 - val_mcc: 0.8534
Epoch 23/100
63/63 [==============================] - 7s 107ms/step - loss: 0.4903 - accuracy: 0.8397 - recall: 0.7965 - precision: 0.8838 - AUROC: 0.9842 - AUPRC: 0.9142 - f1_score: 0.8379 - balanced_accuracy: 0.8924 - specificity: 0.9884 - miss_rate: 0.2035 - fall_out: 0.0116 - mcc: 0.8222 - val_loss: 0.4336 - val_accuracy: 0.8618 - val_recall: 0.8322 - val_precision: 0.9003 - val_AUROC: 0.9871 - val_AUPRC: 0.9351 - val_f1_score: 0.8649 - val_balanced_accuracy: 0.9110 - val_specificity: 0.9898 - val_miss_rate: 0.1678 - val_fall_out: 0.0102 - val_mcc: 0.8514
Epoch 24/100
63/63 [==============================] - 7s 106ms/step - loss: 0.4901 - accuracy: 0.8364 - recall: 0.7993 - precision: 0.8780 - AUROC: 0.9847 - AUPRC: 0.9136 - f1_score: 0.8368 - balanced_accuracy: 0.8935 - specificity: 0.9877 - miss_rate: 0.2007 - fall_out: 0.0123 - mcc: 0.8207 - val_loss: 0.4251 - val_accuracy: 0.8668 - val_recall: 0.8307 - val_precision: 0.9125 - val_AUROC: 0.9871 - val_AUPRC: 0.9371 - val_f1_score: 0.8697 - val_balanced_accuracy: 0.9109 - val_specificity: 0.9912 - val_miss_rate: 0.1693 - val_fall_out: 0.0088 - val_mcc: 0.8572
Epoch 25/100
63/63 [==============================] - 7s 106ms/step - loss: 0.4434 - accuracy: 0.8521 - recall: 0.8146 - precision: 0.8896 - AUROC: 0.9873 - AUPRC: 0.9274 - f1_score: 0.8505 - balanced_accuracy: 0.9017 - specificity: 0.9888 - miss_rate: 0.1854 - fall_out: 0.0112 - mcc: 0.8357 - val_loss: 0.4223 - val_accuracy: 0.8638 - val_recall: 0.8358 - val_precision: 0.9027 - val_AUROC: 0.9870 - val_AUPRC: 0.9364 - val_f1_score: 0.8679 - val_balanced_accuracy: 0.9129 - val_specificity: 0.9900 - val_miss_rate: 0.1642 - val_fall_out: 0.0100 - val_mcc: 0.8546
Epoch 26/100
63/63 [==============================] - 7s 106ms/step - loss: 0.4227 - accuracy: 0.8600 - recall: 0.8258 - precision: 0.8952 - AUROC: 0.9879 - AUPRC: 0.9323 - f1_score: 0.8591 - balanced_accuracy: 0.9075 - specificity: 0.9893 - miss_rate: 0.1742 - fall_out: 0.0107 - mcc: 0.8450 - val_loss: 0.4336 - val_accuracy: 0.8593 - val_recall: 0.8282 - val_precision: 0.8994 - val_AUROC: 0.9866 - val_AUPRC: 0.9339 - val_f1_score: 0.8624 - val_balanced_accuracy: 0.9090 - val_specificity: 0.9897 - val_miss_rate: 0.1718 - val_fall_out: 0.0103 - val_mcc: 0.8486
Epoch 27/100
63/63 [==============================] - 7s 107ms/step - loss: 0.4012 - accuracy: 0.8687 - recall: 0.8359 - precision: 0.8989 - AUROC: 0.9887 - AUPRC: 0.9382 - f1_score: 0.8662 - balanced_accuracy: 0.9127 - specificity: 0.9895 - miss_rate: 0.1641 - fall_out: 0.0105 - mcc: 0.8527 - val_loss: 0.4225 - val_accuracy: 0.8698 - val_recall: 0.8513 - val_precision: 0.8990 - val_AUROC: 0.9861 - val_AUPRC: 0.9360 - val_f1_score: 0.8745 - val_balanced_accuracy: 0.9203 - val_specificity: 0.9894 - val_miss_rate: 0.1487 - val_fall_out: 0.0106 - val_mcc: 0.8614
63/63 [==============================] - 4s 62ms/step - loss: 0.1714 - accuracy: 0.9495 - recall: 0.9330 - precision: 0.9651 - AUROC: 0.9984 - AUPRC: 0.9892 - f1_score: 0.9488 - balanced_accuracy: 0.9646 - specificity: 0.9963 - miss_rate: 0.0670 - fall_out: 0.0037 - mcc: 0.9434
16/16 [==============================] - 3s 180ms/step - loss: 0.4225 - accuracy: 0.8698 - recall: 0.8513 - precision: 0.8990 - AUROC: 0.9861 - AUPRC: 0.9360 - f1_score: 0.8745 - balanced_accuracy: 0.9203 - specificity: 0.9894 - miss_rate: 0.1487 - fall_out: 0.0106 - mcc: 0.8614
-- HOLDOUT 8 --
-- 6 Uncorrelated features: [Pearson+Spearman]
['mfcc10_var', 'mfcc17_mean', 'mfcc16_mean', 'mfcc11_var', 'tempo', 'mfcc19_mean']
-- Actually uncorrelated features: [confirmed via MIC]
[]
-- 1 Correlated features: [Pearson]
['spectral_centroid_mean']
- Removing uncorrelated/correlated features: ['spectral_centroid_mean']
- Building Fixed MLP:
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
features_data (InputLayer) [(None, 56)] 0
dense (Dense) (None, 256) 14592
dropout (Dropout) (None, 256) 0
dense_1 (Dense) (None, 256) 65792
dropout_1 (Dropout) (None, 256) 0
dense_2 (Dense) (None, 128) 32896
dropout_2 (Dropout) (None, 128) 0
dense_3 (Dense) (None, 128) 16512
dropout_3 (Dropout) (None, 128) 0
dense_4 (Dense) (None, 10) 1290
=================================================================
Total params: 131,082
Trainable params: 131,082
Non-trainable params: 0
_________________________________________________________________
- Building Fixed CNN:
Model: "model_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
mfccs_data (InputLayer) [(None, 20, 130, 1)] 0
conv2d (Conv2D) (None, 20, 130, 256) 2560
max_pooling2d (MaxPooling2D (None, 10, 65, 256) 0
)
dropout_4 (Dropout) (None, 10, 65, 256) 0
conv2d_1 (Conv2D) (None, 10, 65, 256) 590080
max_pooling2d_1 (MaxPooling (None, 5, 33, 256) 0
2D)
dropout_5 (Dropout) (None, 5, 33, 256) 0
conv2d_2 (Conv2D) (None, 5, 33, 256) 590080
max_pooling2d_2 (MaxPooling (None, 3, 17, 256) 0
2D)
dropout_6 (Dropout) (None, 3, 17, 256) 0
conv2d_3 (Conv2D) (None, 3, 17, 512) 2097664
max_pooling2d_3 (MaxPooling (None, 2, 9, 512) 0
2D)
flatten (Flatten) (None, 9216) 0
dense_5 (Dense) (None, 128) 1179776
dropout_7 (Dropout) (None, 128) 0
dense_6 (Dense) (None, 128) 16512
dropout_8 (Dropout) (None, 128) 0
dense_7 (Dense) (None, 10) 1290
=================================================================
Total params: 4,477,962
Trainable params: 4,477,962
Non-trainable params: 0
_________________________________________________________________
- Building MMNN:
Model: "model_2"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
mfccs_data (InputLayer) [(None, 20, 130, 1) 0 []
]
conv2d (Conv2D) (None, 20, 130, 256 2560 ['mfccs_data[0][0]']
)
max_pooling2d (MaxPooling2D) (None, 10, 65, 256) 0 ['conv2d[0][0]']
dropout_4 (Dropout) (None, 10, 65, 256) 0 ['max_pooling2d[0][0]']
conv2d_1 (Conv2D) (None, 10, 65, 256) 590080 ['dropout_4[0][0]']
max_pooling2d_1 (MaxPooling2D) (None, 5, 33, 256) 0 ['conv2d_1[0][0]']
dropout_5 (Dropout) (None, 5, 33, 256) 0 ['max_pooling2d_1[0][0]']
conv2d_2 (Conv2D) (None, 5, 33, 256) 590080 ['dropout_5[0][0]']
features_data (InputLayer) [(None, 56)] 0 []
max_pooling2d_2 (MaxPooling2D) (None, 3, 17, 256) 0 ['conv2d_2[0][0]']
dense (Dense) (None, 256) 14592 ['features_data[0][0]']
dropout_6 (Dropout) (None, 3, 17, 256) 0 ['max_pooling2d_2[0][0]']
dropout (Dropout) (None, 256) 0 ['dense[0][0]']
conv2d_3 (Conv2D) (None, 3, 17, 512) 2097664 ['dropout_6[0][0]']
dense_1 (Dense) (None, 256) 65792 ['dropout[0][0]']
max_pooling2d_3 (MaxPooling2D) (None, 2, 9, 512) 0 ['conv2d_3[0][0]']
dropout_1 (Dropout) (None, 256) 0 ['dense_1[0][0]']
flatten (Flatten) (None, 9216) 0 ['max_pooling2d_3[0][0]']
dense_2 (Dense) (None, 128) 32896 ['dropout_1[0][0]']
dense_5 (Dense) (None, 128) 1179776 ['flatten[0][0]']
dropout_2 (Dropout) (None, 128) 0 ['dense_2[0][0]']
dropout_7 (Dropout) (None, 128) 0 ['dense_5[0][0]']
dense_3 (Dense) (None, 128) 16512 ['dropout_2[0][0]']
dense_6 (Dense) (None, 128) 16512 ['dropout_7[0][0]']
dropout_3 (Dropout) (None, 128) 0 ['dense_3[0][0]']
dropout_8 (Dropout) (None, 128) 0 ['dense_6[0][0]']
concatenate (Concatenate) (None, 256) 0 ['dropout_3[0][0]',
'dropout_8[0][0]']
dense_8 (Dense) (None, 64) 16448 ['concatenate[0][0]']
dense_9 (Dense) (None, 10) 650 ['dense_8[0][0]']
==================================================================================================
Total params: 4,623,562
Trainable params: 4,623,562
Non-trainable params: 0
__________________________________________________________________________________________________
- Training MMNN model:
Epoch 1/100
63/63 [==============================] - 9s 113ms/step - loss: 2.1875 - accuracy: 0.1978 - recall: 0.0172 - precision: 0.4660 - AUROC: 0.6563 - AUPRC: 0.1876 - f1_score: 0.0331 - balanced_accuracy: 0.5075 - specificity: 0.9978 - miss_rate: 0.9828 - fall_out: 0.0022 - mcc: 0.0742 - val_loss: 1.6729 - val_accuracy: 0.3731 - val_recall: 0.1092 - val_precision: 0.8289 - val_AUROC: 0.8439 - val_AUPRC: 0.4294 - val_f1_score: 0.1929 - val_balanced_accuracy: 0.5533 - val_specificity: 0.9975 - val_miss_rate: 0.8908 - val_fall_out: 0.0025 - val_mcc: 0.2807
Epoch 2/100
63/63 [==============================] - 7s 107ms/step - loss: 1.6853 - accuracy: 0.3788 - recall: 0.1498 - precision: 0.6181 - AUROC: 0.8340 - AUPRC: 0.3904 - f1_score: 0.2412 - balanced_accuracy: 0.5698 - specificity: 0.9897 - miss_rate: 0.8502 - fall_out: 0.0103 - mcc: 0.2722 - val_loss: 1.3632 - val_accuracy: 0.4752 - val_recall: 0.2814 - val_precision: 0.6896 - val_AUROC: 0.8978 - val_AUPRC: 0.5529 - val_f1_score: 0.3997 - val_balanced_accuracy: 0.6337 - val_specificity: 0.9859 - val_miss_rate: 0.7186 - val_fall_out: 0.0141 - val_mcc: 0.4054
Epoch 3/100
63/63 [==============================] - 7s 108ms/step - loss: 1.4794 - accuracy: 0.4552 - recall: 0.2415 - precision: 0.6610 - AUROC: 0.8769 - AUPRC: 0.4871 - f1_score: 0.3537 - balanced_accuracy: 0.6139 - specificity: 0.9862 - miss_rate: 0.7585 - fall_out: 0.0138 - mcc: 0.3641 - val_loss: 1.1793 - val_accuracy: 0.5719 - val_recall: 0.3230 - val_precision: 0.8073 - val_AUROC: 0.9285 - val_AUPRC: 0.6461 - val_f1_score: 0.4614 - val_balanced_accuracy: 0.6572 - val_specificity: 0.9914 - val_miss_rate: 0.6770 - val_fall_out: 0.0086 - val_mcc: 0.4813
Epoch 4/100
63/63 [==============================] - 7s 108ms/step - loss: 1.3350 - accuracy: 0.5144 - recall: 0.3120 - precision: 0.6962 - AUROC: 0.9008 - AUPRC: 0.5599 - f1_score: 0.4309 - balanced_accuracy: 0.6484 - specificity: 0.9849 - miss_rate: 0.6880 - fall_out: 0.0151 - mcc: 0.4305 - val_loss: 1.0664 - val_accuracy: 0.6184 - val_recall: 0.3660 - val_precision: 0.8149 - val_AUROC: 0.9439 - val_AUPRC: 0.6986 - val_f1_score: 0.5052 - val_balanced_accuracy: 0.6784 - val_specificity: 0.9908 - val_miss_rate: 0.6340 - val_fall_out: 0.0092 - val_mcc: 0.5168
Epoch 5/100
63/63 [==============================] - 7s 107ms/step - loss: 1.2085 - accuracy: 0.5624 - recall: 0.3735 - precision: 0.7247 - AUROC: 0.9184 - AUPRC: 0.6188 - f1_score: 0.4929 - balanced_accuracy: 0.6789 - specificity: 0.9842 - miss_rate: 0.6265 - fall_out: 0.0158 - mcc: 0.4854 - val_loss: 0.9651 - val_accuracy: 0.6530 - val_recall: 0.4722 - val_precision: 0.8143 - val_AUROC: 0.9515 - val_AUPRC: 0.7388 - val_f1_score: 0.5978 - val_balanced_accuracy: 0.7301 - val_specificity: 0.9880 - val_miss_rate: 0.5278 - val_fall_out: 0.0120 - val_mcc: 0.5908
Epoch 6/100
63/63 [==============================] - 7s 107ms/step - loss: 1.1296 - accuracy: 0.6011 - recall: 0.4251 - precision: 0.7365 - AUROC: 0.9285 - AUPRC: 0.6563 - f1_score: 0.5391 - balanced_accuracy: 0.7041 - specificity: 0.9831 - miss_rate: 0.5749 - fall_out: 0.0169 - mcc: 0.5251 - val_loss: 0.8927 - val_accuracy: 0.6900 - val_recall: 0.5008 - val_precision: 0.8389 - val_AUROC: 0.9591 - val_AUPRC: 0.7799 - val_f1_score: 0.6272 - val_balanced_accuracy: 0.7450 - val_specificity: 0.9893 - val_miss_rate: 0.4992 - val_fall_out: 0.0107 - val_mcc: 0.6206
Epoch 7/100
63/63 [==============================] - 7s 107ms/step - loss: 1.0693 - accuracy: 0.6263 - recall: 0.4652 - precision: 0.7547 - AUROC: 0.9356 - AUPRC: 0.6884 - f1_score: 0.5756 - balanced_accuracy: 0.7242 - specificity: 0.9832 - miss_rate: 0.5348 - fall_out: 0.0168 - mcc: 0.5593 - val_loss: 0.8561 - val_accuracy: 0.7016 - val_recall: 0.5448 - val_precision: 0.8249 - val_AUROC: 0.9612 - val_AUPRC: 0.7869 - val_f1_score: 0.6562 - val_balanced_accuracy: 0.7660 - val_specificity: 0.9871 - val_miss_rate: 0.4552 - val_fall_out: 0.0129 - val_mcc: 0.6426
Epoch 8/100
63/63 [==============================] - 7s 107ms/step - loss: 0.9781 - accuracy: 0.6587 - recall: 0.5170 - precision: 0.7773 - AUROC: 0.9458 - AUPRC: 0.7318 - f1_score: 0.6210 - balanced_accuracy: 0.7503 - specificity: 0.9835 - miss_rate: 0.4830 - fall_out: 0.0165 - mcc: 0.6026 - val_loss: 0.7599 - val_accuracy: 0.7441 - val_recall: 0.5929 - val_precision: 0.8636 - val_AUROC: 0.9695 - val_AUPRC: 0.8287 - val_f1_score: 0.7031 - val_balanced_accuracy: 0.7912 - val_specificity: 0.9896 - val_miss_rate: 0.4071 - val_fall_out: 0.0104 - val_mcc: 0.6911
Epoch 9/100
63/63 [==============================] - 7s 107ms/step - loss: 0.8980 - accuracy: 0.6890 - recall: 0.5670 - precision: 0.7914 - AUROC: 0.9538 - AUPRC: 0.7678 - f1_score: 0.6607 - balanced_accuracy: 0.7752 - specificity: 0.9834 - miss_rate: 0.4330 - fall_out: 0.0166 - mcc: 0.6403 - val_loss: 0.8071 - val_accuracy: 0.7256 - val_recall: 0.6214 - val_precision: 0.8357 - val_AUROC: 0.9626 - val_AUPRC: 0.8119 - val_f1_score: 0.7128 - val_balanced_accuracy: 0.8039 - val_specificity: 0.9864 - val_miss_rate: 0.3786 - val_fall_out: 0.0136 - val_mcc: 0.6951
Epoch 10/100
63/63 [==============================] - 7s 107ms/step - loss: 0.8863 - accuracy: 0.6934 - recall: 0.5784 - precision: 0.7889 - AUROC: 0.9546 - AUPRC: 0.7730 - f1_score: 0.6674 - balanced_accuracy: 0.7806 - specificity: 0.9828 - miss_rate: 0.4216 - fall_out: 0.0172 - mcc: 0.6459 - val_loss: 0.6850 - val_accuracy: 0.7656 - val_recall: 0.6570 - val_precision: 0.8747 - val_AUROC: 0.9744 - val_AUPRC: 0.8570 - val_f1_score: 0.7504 - val_balanced_accuracy: 0.8233 - val_specificity: 0.9895 - val_miss_rate: 0.3430 - val_fall_out: 0.0105 - val_mcc: 0.7359
Epoch 11/100
63/63 [==============================] - 7s 107ms/step - loss: 0.8311 - accuracy: 0.7147 - recall: 0.6195 - precision: 0.8107 - AUROC: 0.9602 - AUPRC: 0.7956 - f1_score: 0.7023 - balanced_accuracy: 0.8017 - specificity: 0.9839 - miss_rate: 0.3805 - fall_out: 0.0161 - mcc: 0.6814 - val_loss: 0.6231 - val_accuracy: 0.7797 - val_recall: 0.7091 - val_precision: 0.8603 - val_AUROC: 0.9780 - val_AUPRC: 0.8737 - val_f1_score: 0.7774 - val_balanced_accuracy: 0.8481 - val_specificity: 0.9872 - val_miss_rate: 0.2909 - val_fall_out: 0.0128 - val_mcc: 0.7595
Epoch 12/100
63/63 [==============================] - 7s 108ms/step - loss: 0.7754 - accuracy: 0.7380 - recall: 0.6413 - precision: 0.8197 - AUROC: 0.9647 - AUPRC: 0.8190 - f1_score: 0.7196 - balanced_accuracy: 0.8128 - specificity: 0.9843 - miss_rate: 0.3587 - fall_out: 0.0157 - mcc: 0.6989 - val_loss: 0.6474 - val_accuracy: 0.7822 - val_recall: 0.6930 - val_precision: 0.8623 - val_AUROC: 0.9758 - val_AUPRC: 0.8689 - val_f1_score: 0.7685 - val_balanced_accuracy: 0.8404 - val_specificity: 0.9877 - val_miss_rate: 0.3070 - val_fall_out: 0.0123 - val_mcc: 0.7512
Epoch 13/100
63/63 [==============================] - 7s 107ms/step - loss: 0.7470 - accuracy: 0.7442 - recall: 0.6677 - precision: 0.8260 - AUROC: 0.9672 - AUPRC: 0.8275 - f1_score: 0.7385 - balanced_accuracy: 0.8260 - specificity: 0.9844 - miss_rate: 0.3323 - fall_out: 0.0156 - mcc: 0.7177 - val_loss: 0.6092 - val_accuracy: 0.7897 - val_recall: 0.7091 - val_precision: 0.8629 - val_AUROC: 0.9791 - val_AUPRC: 0.8783 - val_f1_score: 0.7784 - val_balanced_accuracy: 0.8483 - val_specificity: 0.9875 - val_miss_rate: 0.2909 - val_fall_out: 0.0125 - val_mcc: 0.7609
Epoch 14/100
63/63 [==============================] - 7s 107ms/step - loss: 0.7005 - accuracy: 0.7670 - recall: 0.6864 - precision: 0.8377 - AUROC: 0.9708 - AUPRC: 0.8443 - f1_score: 0.7545 - balanced_accuracy: 0.8358 - specificity: 0.9852 - miss_rate: 0.3136 - fall_out: 0.0148 - mcc: 0.7346 - val_loss: 0.5377 - val_accuracy: 0.8157 - val_recall: 0.7586 - val_precision: 0.8793 - val_AUROC: 0.9829 - val_AUPRC: 0.9011 - val_f1_score: 0.8145 - val_balanced_accuracy: 0.8735 - val_specificity: 0.9884 - val_miss_rate: 0.2414 - val_fall_out: 0.0116 - val_mcc: 0.7982
Epoch 15/100
63/63 [==============================] - 7s 107ms/step - loss: 0.6460 - accuracy: 0.7847 - recall: 0.7191 - precision: 0.8449 - AUROC: 0.9748 - AUPRC: 0.8664 - f1_score: 0.7769 - balanced_accuracy: 0.8522 - specificity: 0.9853 - miss_rate: 0.2809 - fall_out: 0.0147 - mcc: 0.7573 - val_loss: 0.5391 - val_accuracy: 0.8167 - val_recall: 0.7496 - val_precision: 0.8811 - val_AUROC: 0.9831 - val_AUPRC: 0.9004 - val_f1_score: 0.8101 - val_balanced_accuracy: 0.8692 - val_specificity: 0.9888 - val_miss_rate: 0.2504 - val_fall_out: 0.0112 - val_mcc: 0.7940
Epoch 16/100
63/63 [==============================] - 7s 107ms/step - loss: 0.6181 - accuracy: 0.7968 - recall: 0.7287 - precision: 0.8560 - AUROC: 0.9766 - AUPRC: 0.8748 - f1_score: 0.7872 - balanced_accuracy: 0.8575 - specificity: 0.9864 - miss_rate: 0.2713 - fall_out: 0.0136 - mcc: 0.7687 - val_loss: 0.6147 - val_accuracy: 0.7917 - val_recall: 0.7246 - val_precision: 0.8497 - val_AUROC: 0.9777 - val_AUPRC: 0.8771 - val_f1_score: 0.7822 - val_balanced_accuracy: 0.8552 - val_specificity: 0.9858 - val_miss_rate: 0.2754 - val_fall_out: 0.0142 - val_mcc: 0.7630
63/63 [==============================] - 4s 64ms/step - loss: 0.4562 - accuracy: 0.8521 - recall: 0.7851 - precision: 0.9080 - AUROC: 0.9890 - AUPRC: 0.9312 - f1_score: 0.8421 - balanced_accuracy: 0.8881 - specificity: 0.9912 - miss_rate: 0.2149 - fall_out: 0.0088 - mcc: 0.8286
16/16 [==============================] - 3s 193ms/step - loss: 0.6147 - accuracy: 0.7917 - recall: 0.7246 - precision: 0.8497 - AUROC: 0.9777 - AUPRC: 0.8771 - f1_score: 0.7822 - balanced_accuracy: 0.8552 - specificity: 0.9858 - miss_rate: 0.2754 - fall_out: 0.0142 - mcc: 0.7630
-- HOLDOUT 9 --
-- 6 Uncorrelated features: [Pearson+Spearman]
['mfcc10_var', 'mfcc17_mean', 'mfcc16_mean', 'mfcc11_var', 'tempo', 'mfcc19_mean']
-- Actually uncorrelated features: [confirmed via MIC]
[]
-- 1 Correlated features: [Pearson]
['spectral_centroid_mean']
- Removing uncorrelated/correlated features: ['spectral_centroid_mean']
- Building Fixed MLP:
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
features_data (InputLayer) [(None, 56)] 0
dense (Dense) (None, 256) 14592
dropout (Dropout) (None, 256) 0
dense_1 (Dense) (None, 256) 65792
dropout_1 (Dropout) (None, 256) 0
dense_2 (Dense) (None, 128) 32896
dropout_2 (Dropout) (None, 128) 0
dense_3 (Dense) (None, 128) 16512
dropout_3 (Dropout) (None, 128) 0
dense_4 (Dense) (None, 10) 1290
=================================================================
Total params: 131,082
Trainable params: 131,082
Non-trainable params: 0
_________________________________________________________________
- Building Fixed CNN:
Model: "model_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
mfccs_data (InputLayer) [(None, 20, 130, 1)] 0
conv2d (Conv2D) (None, 20, 130, 256) 2560
max_pooling2d (MaxPooling2D (None, 10, 65, 256) 0
)
dropout_4 (Dropout) (None, 10, 65, 256) 0
conv2d_1 (Conv2D) (None, 10, 65, 256) 590080
max_pooling2d_1 (MaxPooling (None, 5, 33, 256) 0
2D)
dropout_5 (Dropout) (None, 5, 33, 256) 0
conv2d_2 (Conv2D) (None, 5, 33, 256) 590080
max_pooling2d_2 (MaxPooling (None, 3, 17, 256) 0
2D)
dropout_6 (Dropout) (None, 3, 17, 256) 0
conv2d_3 (Conv2D) (None, 3, 17, 512) 2097664
max_pooling2d_3 (MaxPooling (None, 2, 9, 512) 0
2D)
flatten (Flatten) (None, 9216) 0
dense_5 (Dense) (None, 128) 1179776
dropout_7 (Dropout) (None, 128) 0
dense_6 (Dense) (None, 128) 16512
dropout_8 (Dropout) (None, 128) 0
dense_7 (Dense) (None, 10) 1290
=================================================================
Total params: 4,477,962
Trainable params: 4,477,962
Non-trainable params: 0
_________________________________________________________________
- Building MMNN:
Model: "model_2"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
mfccs_data (InputLayer) [(None, 20, 130, 1) 0 []
]
conv2d (Conv2D) (None, 20, 130, 256 2560 ['mfccs_data[0][0]']
)
max_pooling2d (MaxPooling2D) (None, 10, 65, 256) 0 ['conv2d[0][0]']
dropout_4 (Dropout) (None, 10, 65, 256) 0 ['max_pooling2d[0][0]']
conv2d_1 (Conv2D) (None, 10, 65, 256) 590080 ['dropout_4[0][0]']
max_pooling2d_1 (MaxPooling2D) (None, 5, 33, 256) 0 ['conv2d_1[0][0]']
dropout_5 (Dropout) (None, 5, 33, 256) 0 ['max_pooling2d_1[0][0]']
conv2d_2 (Conv2D) (None, 5, 33, 256) 590080 ['dropout_5[0][0]']
features_data (InputLayer) [(None, 56)] 0 []
max_pooling2d_2 (MaxPooling2D) (None, 3, 17, 256) 0 ['conv2d_2[0][0]']
dense (Dense) (None, 256) 14592 ['features_data[0][0]']
dropout_6 (Dropout) (None, 3, 17, 256) 0 ['max_pooling2d_2[0][0]']
dropout (Dropout) (None, 256) 0 ['dense[0][0]']
conv2d_3 (Conv2D) (None, 3, 17, 512) 2097664 ['dropout_6[0][0]']
dense_1 (Dense) (None, 256) 65792 ['dropout[0][0]']
max_pooling2d_3 (MaxPooling2D) (None, 2, 9, 512) 0 ['conv2d_3[0][0]']
dropout_1 (Dropout) (None, 256) 0 ['dense_1[0][0]']
flatten (Flatten) (None, 9216) 0 ['max_pooling2d_3[0][0]']
dense_2 (Dense) (None, 128) 32896 ['dropout_1[0][0]']
dense_5 (Dense) (None, 128) 1179776 ['flatten[0][0]']
dropout_2 (Dropout) (None, 128) 0 ['dense_2[0][0]']
dropout_7 (Dropout) (None, 128) 0 ['dense_5[0][0]']
dense_3 (Dense) (None, 128) 16512 ['dropout_2[0][0]']
dense_6 (Dense) (None, 128) 16512 ['dropout_7[0][0]']
dropout_3 (Dropout) (None, 128) 0 ['dense_3[0][0]']
dropout_8 (Dropout) (None, 128) 0 ['dense_6[0][0]']
concatenate (Concatenate) (None, 256) 0 ['dropout_3[0][0]',
'dropout_8[0][0]']
dense_8 (Dense) (None, 64) 16448 ['concatenate[0][0]']
dense_9 (Dense) (None, 10) 650 ['dense_8[0][0]']
==================================================================================================
Total params: 4,623,562
Trainable params: 4,623,562
Non-trainable params: 0
__________________________________________________________________________________________________
- Training MMNN model:
Epoch 1/100
63/63 [==============================] - 9s 114ms/step - loss: 2.1634 - accuracy: 0.2188 - recall: 0.0301 - precision: 0.5042 - AUROC: 0.6703 - AUPRC: 0.2075 - f1_score: 0.0567 - balanced_accuracy: 0.5134 - specificity: 0.9967 - miss_rate: 0.9699 - fall_out: 0.0033 - mcc: 0.1043 - val_loss: 1.6422 - val_accuracy: 0.3701 - val_recall: 0.1733 - val_precision: 0.7149 - val_AUROC: 0.8440 - val_AUPRC: 0.4363 - val_f1_score: 0.2789 - val_balanced_accuracy: 0.5828 - val_specificity: 0.9923 - val_miss_rate: 0.8267 - val_fall_out: 0.0077 - val_mcc: 0.3230
Epoch 2/100
63/63 [==============================] - 7s 108ms/step - loss: 1.6613 - accuracy: 0.3884 - recall: 0.1683 - precision: 0.6394 - AUROC: 0.8398 - AUPRC: 0.4090 - f1_score: 0.2665 - balanced_accuracy: 0.5789 - specificity: 0.9895 - miss_rate: 0.8317 - fall_out: 0.0105 - mcc: 0.2957 - val_loss: 1.3631 - val_accuracy: 0.5033 - val_recall: 0.2434 - val_precision: 0.7941 - val_AUROC: 0.8994 - val_AUPRC: 0.5627 - val_f1_score: 0.3726 - val_balanced_accuracy: 0.6182 - val_specificity: 0.9930 - val_miss_rate: 0.7566 - val_fall_out: 0.0070 - val_mcc: 0.4114
Epoch 3/100
63/63 [==============================] - 7s 108ms/step - loss: 1.4851 - accuracy: 0.4636 - recall: 0.2367 - precision: 0.6714 - AUROC: 0.8747 - AUPRC: 0.4871 - f1_score: 0.3500 - balanced_accuracy: 0.6119 - specificity: 0.9871 - miss_rate: 0.7633 - fall_out: 0.0129 - mcc: 0.3641 - val_loss: 1.2784 - val_accuracy: 0.5233 - val_recall: 0.3280 - val_precision: 0.7511 - val_AUROC: 0.9095 - val_AUPRC: 0.6053 - val_f1_score: 0.4566 - val_balanced_accuracy: 0.6580 - val_specificity: 0.9879 - val_miss_rate: 0.6720 - val_fall_out: 0.0121 - val_mcc: 0.4638
Epoch 4/100
63/63 [==============================] - 7s 108ms/step - loss: 1.3485 - accuracy: 0.5113 - recall: 0.2937 - precision: 0.7091 - AUROC: 0.8978 - AUPRC: 0.5525 - f1_score: 0.4154 - balanced_accuracy: 0.6402 - specificity: 0.9866 - miss_rate: 0.7063 - fall_out: 0.0134 - mcc: 0.4220 - val_loss: 1.0800 - val_accuracy: 0.6169 - val_recall: 0.3891 - val_precision: 0.7920 - val_AUROC: 0.9375 - val_AUPRC: 0.6922 - val_f1_score: 0.5218 - val_balanced_accuracy: 0.6889 - val_specificity: 0.9886 - val_miss_rate: 0.6109 - val_fall_out: 0.0114 - val_mcc: 0.5243
Epoch 5/100
63/63 [==============================] - 7s 108ms/step - loss: 1.2139 - accuracy: 0.5706 - recall: 0.3660 - precision: 0.7397 - AUROC: 0.9172 - AUPRC: 0.6201 - f1_score: 0.4897 - balanced_accuracy: 0.6758 - specificity: 0.9857 - miss_rate: 0.6340 - fall_out: 0.0143 - mcc: 0.4865 - val_loss: 1.0080 - val_accuracy: 0.6375 - val_recall: 0.4622 - val_precision: 0.7789 - val_AUROC: 0.9443 - val_AUPRC: 0.7245 - val_f1_score: 0.5801 - val_balanced_accuracy: 0.7238 - val_specificity: 0.9854 - val_miss_rate: 0.5378 - val_fall_out: 0.0146 - val_mcc: 0.5684
Epoch 6/100
63/63 [==============================] - 7s 108ms/step - loss: 1.1282 - accuracy: 0.6057 - recall: 0.4272 - precision: 0.7546 - AUROC: 0.9285 - AUPRC: 0.6691 - f1_score: 0.5456 - balanced_accuracy: 0.7059 - specificity: 0.9846 - miss_rate: 0.5728 - fall_out: 0.0154 - mcc: 0.5346 - val_loss: 0.8983 - val_accuracy: 0.6915 - val_recall: 0.5208 - val_precision: 0.8300 - val_AUROC: 0.9559 - val_AUPRC: 0.7756 - val_f1_score: 0.6400 - val_balanced_accuracy: 0.7545 - val_specificity: 0.9881 - val_miss_rate: 0.4792 - val_fall_out: 0.0119 - val_mcc: 0.6296
Epoch 7/100
63/63 [==============================] - 7s 108ms/step - loss: 1.0530 - accuracy: 0.6366 - recall: 0.4709 - precision: 0.7706 - AUROC: 0.9370 - AUPRC: 0.7016 - f1_score: 0.5846 - balanced_accuracy: 0.7277 - specificity: 0.9844 - miss_rate: 0.5291 - fall_out: 0.0156 - mcc: 0.5703 - val_loss: 0.8600 - val_accuracy: 0.7071 - val_recall: 0.5463 - val_precision: 0.8405 - val_AUROC: 0.9598 - val_AUPRC: 0.7953 - val_f1_score: 0.6622 - val_balanced_accuracy: 0.7674 - val_specificity: 0.9885 - val_miss_rate: 0.4537 - val_fall_out: 0.0115 - val_mcc: 0.6508
Epoch 8/100
63/63 [==============================] - 7s 109ms/step - loss: 0.9888 - accuracy: 0.6604 - recall: 0.5188 - precision: 0.7792 - AUROC: 0.9446 - AUPRC: 0.7279 - f1_score: 0.6229 - balanced_accuracy: 0.7512 - specificity: 0.9837 - miss_rate: 0.4812 - fall_out: 0.0163 - mcc: 0.6046 - val_loss: 0.8191 - val_accuracy: 0.7176 - val_recall: 0.5839 - val_precision: 0.8346 - val_AUROC: 0.9632 - val_AUPRC: 0.8090 - val_f1_score: 0.6871 - val_balanced_accuracy: 0.7855 - val_specificity: 0.9871 - val_miss_rate: 0.4161 - val_fall_out: 0.0129 - val_mcc: 0.6716
Epoch 9/100
63/63 [==============================] - 7s 108ms/step - loss: 0.9332 - accuracy: 0.6916 - recall: 0.5562 - precision: 0.8022 - AUROC: 0.9500 - AUPRC: 0.7586 - f1_score: 0.6570 - balanced_accuracy: 0.7705 - specificity: 0.9848 - miss_rate: 0.4438 - fall_out: 0.0152 - mcc: 0.6389 - val_loss: 0.7732 - val_accuracy: 0.7306 - val_recall: 0.6119 - val_precision: 0.8246 - val_AUROC: 0.9663 - val_AUPRC: 0.8211 - val_f1_score: 0.7025 - val_balanced_accuracy: 0.7987 - val_specificity: 0.9855 - val_miss_rate: 0.3881 - val_fall_out: 0.0145 - val_mcc: 0.6838
Epoch 10/100
63/63 [==============================] - 7s 108ms/step - loss: 0.8993 - accuracy: 0.6950 - recall: 0.5704 - precision: 0.7967 - AUROC: 0.9536 - AUPRC: 0.7722 - f1_score: 0.6648 - balanced_accuracy: 0.7771 - specificity: 0.9838 - miss_rate: 0.4296 - fall_out: 0.0162 - mcc: 0.6449 - val_loss: 0.7416 - val_accuracy: 0.7471 - val_recall: 0.6324 - val_precision: 0.8403 - val_AUROC: 0.9688 - val_AUPRC: 0.8359 - val_f1_score: 0.7217 - val_balanced_accuracy: 0.8095 - val_specificity: 0.9866 - val_miss_rate: 0.3676 - val_fall_out: 0.0134 - val_mcc: 0.7040
Epoch 11/100
63/63 [==============================] - 7s 108ms/step - loss: 0.8409 - accuracy: 0.7178 - recall: 0.6053 - precision: 0.8132 - AUROC: 0.9588 - AUPRC: 0.7934 - f1_score: 0.6940 - balanced_accuracy: 0.7949 - specificity: 0.9846 - miss_rate: 0.3947 - fall_out: 0.0154 - mcc: 0.6742 - val_loss: 0.7128 - val_accuracy: 0.7606 - val_recall: 0.6780 - val_precision: 0.8368 - val_AUROC: 0.9697 - val_AUPRC: 0.8451 - val_f1_score: 0.7491 - val_balanced_accuracy: 0.8317 - val_specificity: 0.9853 - val_miss_rate: 0.3220 - val_fall_out: 0.0147 - val_mcc: 0.7293
Epoch 12/100
63/63 [==============================] - 7s 108ms/step - loss: 0.8051 - accuracy: 0.7301 - recall: 0.6290 - precision: 0.8169 - AUROC: 0.9618 - AUPRC: 0.8082 - f1_score: 0.7107 - balanced_accuracy: 0.8067 - specificity: 0.9843 - miss_rate: 0.3710 - fall_out: 0.0157 - mcc: 0.6902 - val_loss: 0.6800 - val_accuracy: 0.7727 - val_recall: 0.6855 - val_precision: 0.8551 - val_AUROC: 0.9731 - val_AUPRC: 0.8586 - val_f1_score: 0.7610 - val_balanced_accuracy: 0.8363 - val_specificity: 0.9871 - val_miss_rate: 0.3145 - val_fall_out: 0.0129 - val_mcc: 0.7431
Epoch 13/100
63/63 [==============================] - 7s 109ms/step - loss: 0.7780 - accuracy: 0.7387 - recall: 0.6450 - precision: 0.8210 - AUROC: 0.9642 - AUPRC: 0.8183 - f1_score: 0.7225 - balanced_accuracy: 0.8147 - specificity: 0.9844 - miss_rate: 0.3550 - fall_out: 0.0156 - mcc: 0.7018 - val_loss: 0.6392 - val_accuracy: 0.7867 - val_recall: 0.7056 - val_precision: 0.8597 - val_AUROC: 0.9757 - val_AUPRC: 0.8754 - val_f1_score: 0.7750 - val_balanced_accuracy: 0.8464 - val_specificity: 0.9872 - val_miss_rate: 0.2944 - val_fall_out: 0.0128 - val_mcc: 0.7572
Epoch 14/100
63/63 [==============================] - 7s 109ms/step - loss: 0.7280 - accuracy: 0.7485 - recall: 0.6692 - precision: 0.8348 - AUROC: 0.9685 - AUPRC: 0.8365 - f1_score: 0.7429 - balanced_accuracy: 0.8273 - specificity: 0.9853 - miss_rate: 0.3308 - fall_out: 0.0147 - mcc: 0.7231 - val_loss: 0.6133 - val_accuracy: 0.7987 - val_recall: 0.7326 - val_precision: 0.8616 - val_AUROC: 0.9775 - val_AUPRC: 0.8825 - val_f1_score: 0.7919 - val_balanced_accuracy: 0.8598 - val_specificity: 0.9869 - val_miss_rate: 0.2674 - val_fall_out: 0.0131 - val_mcc: 0.7739
Epoch 15/100
63/63 [==============================] - 7s 108ms/step - loss: 0.7066 - accuracy: 0.7663 - recall: 0.6898 - precision: 0.8403 - AUROC: 0.9696 - AUPRC: 0.8470 - f1_score: 0.7576 - balanced_accuracy: 0.8376 - specificity: 0.9854 - miss_rate: 0.3102 - fall_out: 0.0146 - mcc: 0.7379 - val_loss: 0.5844 - val_accuracy: 0.8077 - val_recall: 0.7471 - val_precision: 0.8664 - val_AUROC: 0.9791 - val_AUPRC: 0.8905 - val_f1_score: 0.8024 - val_balanced_accuracy: 0.8672 - val_specificity: 0.9872 - val_miss_rate: 0.2529 - val_fall_out: 0.0128 - val_mcc: 0.7848
Epoch 16/100
63/63 [==============================] - 7s 108ms/step - loss: 0.6688 - accuracy: 0.7734 - recall: 0.7028 - precision: 0.8440 - AUROC: 0.9730 - AUPRC: 0.8592 - f1_score: 0.7669 - balanced_accuracy: 0.8442 - specificity: 0.9856 - miss_rate: 0.2972 - fall_out: 0.0144 - mcc: 0.7474 - val_loss: 0.5737 - val_accuracy: 0.8157 - val_recall: 0.7431 - val_precision: 0.8638 - val_AUROC: 0.9801 - val_AUPRC: 0.8921 - val_f1_score: 0.7989 - val_balanced_accuracy: 0.8650 - val_specificity: 0.9870 - val_miss_rate: 0.2569 - val_fall_out: 0.0130 - val_mcc: 0.7811
Epoch 17/100
63/63 [==============================] - 7s 108ms/step - loss: 0.6374 - accuracy: 0.7882 - recall: 0.7207 - precision: 0.8487 - AUROC: 0.9751 - AUPRC: 0.8685 - f1_score: 0.7795 - balanced_accuracy: 0.8532 - specificity: 0.9857 - miss_rate: 0.2793 - fall_out: 0.0143 - mcc: 0.7602 - val_loss: 0.5649 - val_accuracy: 0.8162 - val_recall: 0.7541 - val_precision: 0.8786 - val_AUROC: 0.9807 - val_AUPRC: 0.8955 - val_f1_score: 0.8116 - val_balanced_accuracy: 0.8713 - val_specificity: 0.9884 - val_miss_rate: 0.2459 - val_fall_out: 0.0116 - val_mcc: 0.7953
Epoch 18/100
63/63 [==============================] - 7s 109ms/step - loss: 0.6074 - accuracy: 0.7997 - recall: 0.7391 - precision: 0.8588 - AUROC: 0.9773 - AUPRC: 0.8778 - f1_score: 0.7945 - balanced_accuracy: 0.8628 - specificity: 0.9865 - miss_rate: 0.2609 - fall_out: 0.0135 - mcc: 0.7762 - val_loss: 0.5293 - val_accuracy: 0.8182 - val_recall: 0.7787 - val_precision: 0.8673 - val_AUROC: 0.9818 - val_AUPRC: 0.9062 - val_f1_score: 0.8206 - val_balanced_accuracy: 0.8827 - val_specificity: 0.9868 - val_miss_rate: 0.2213 - val_fall_out: 0.0132 - val_mcc: 0.8033
Epoch 19/100
63/63 [==============================] - 7s 109ms/step - loss: 0.6113 - accuracy: 0.7966 - recall: 0.7367 - precision: 0.8518 - AUROC: 0.9765 - AUPRC: 0.8753 - f1_score: 0.7901 - balanced_accuracy: 0.8612 - specificity: 0.9858 - miss_rate: 0.2633 - fall_out: 0.0142 - mcc: 0.7711 - val_loss: 0.5122 - val_accuracy: 0.8262 - val_recall: 0.7932 - val_precision: 0.8751 - val_AUROC: 0.9832 - val_AUPRC: 0.9126 - val_f1_score: 0.8322 - val_balanced_accuracy: 0.8903 - val_specificity: 0.9874 - val_miss_rate: 0.2068 - val_fall_out: 0.0126 - val_mcc: 0.8157
Epoch 20/100
63/63 [==============================] - 7s 111ms/step - loss: 0.5648 - accuracy: 0.8090 - recall: 0.7526 - precision: 0.8614 - AUROC: 0.9800 - AUPRC: 0.8913 - f1_score: 0.8033 - balanced_accuracy: 0.8696 - specificity: 0.9865 - miss_rate: 0.2474 - fall_out: 0.0135 - mcc: 0.7853 - val_loss: 0.4817 - val_accuracy: 0.8332 - val_recall: 0.7877 - val_precision: 0.8857 - val_AUROC: 0.9857 - val_AUPRC: 0.9193 - val_f1_score: 0.8338 - val_balanced_accuracy: 0.8882 - val_specificity: 0.9887 - val_miss_rate: 0.2123 - val_fall_out: 0.0113 - val_mcc: 0.8183
Epoch 21/100
63/63 [==============================] - 7s 108ms/step - loss: 0.5434 - accuracy: 0.8199 - recall: 0.7663 - precision: 0.8705 - AUROC: 0.9813 - AUPRC: 0.8989 - f1_score: 0.8151 - balanced_accuracy: 0.8768 - specificity: 0.9873 - miss_rate: 0.2337 - fall_out: 0.0127 - mcc: 0.7980 - val_loss: 0.4768 - val_accuracy: 0.8448 - val_recall: 0.8027 - val_precision: 0.8911 - val_AUROC: 0.9847 - val_AUPRC: 0.9213 - val_f1_score: 0.8446 - val_balanced_accuracy: 0.8959 - val_specificity: 0.9891 - val_miss_rate: 0.1973 - val_fall_out: 0.0109 - val_mcc: 0.8297
Epoch 22/100
63/63 [==============================] - 7s 108ms/step - loss: 0.5361 - accuracy: 0.8240 - recall: 0.7798 - precision: 0.8739 - AUROC: 0.9813 - AUPRC: 0.8993 - f1_score: 0.8242 - balanced_accuracy: 0.8837 - specificity: 0.9875 - miss_rate: 0.2202 - fall_out: 0.0125 - mcc: 0.8075 - val_loss: 0.4794 - val_accuracy: 0.8458 - val_recall: 0.7952 - val_precision: 0.8911 - val_AUROC: 0.9854 - val_AUPRC: 0.9211 - val_f1_score: 0.8404 - val_balanced_accuracy: 0.8922 - val_specificity: 0.9892 - val_miss_rate: 0.2048 - val_fall_out: 0.0108 - val_mcc: 0.8254
Epoch 23/100
63/63 [==============================] - 8s 109ms/step - loss: 0.5201 - accuracy: 0.8273 - recall: 0.7808 - precision: 0.8741 - AUROC: 0.9830 - AUPRC: 0.9053 - f1_score: 0.8248 - balanced_accuracy: 0.8842 - specificity: 0.9875 - miss_rate: 0.2192 - fall_out: 0.0125 - mcc: 0.8081 - val_loss: 0.4557 - val_accuracy: 0.8478 - val_recall: 0.8132 - val_precision: 0.8943 - val_AUROC: 0.9858 - val_AUPRC: 0.9271 - val_f1_score: 0.8518 - val_balanced_accuracy: 0.9013 - val_specificity: 0.9893 - val_miss_rate: 0.1868 - val_fall_out: 0.0107 - val_mcc: 0.8374
Epoch 24/100
63/63 [==============================] - 7s 108ms/step - loss: 0.4851 - accuracy: 0.8414 - recall: 0.7983 - precision: 0.8852 - AUROC: 0.9847 - AUPRC: 0.9171 - f1_score: 0.8395 - balanced_accuracy: 0.8934 - specificity: 0.9885 - miss_rate: 0.2017 - fall_out: 0.0115 - mcc: 0.8240 - val_loss: 0.4909 - val_accuracy: 0.8458 - val_recall: 0.8022 - val_precision: 0.8950 - val_AUROC: 0.9834 - val_AUPRC: 0.9169 - val_f1_score: 0.8461 - val_balanced_accuracy: 0.8959 - val_specificity: 0.9895 - val_miss_rate: 0.1978 - val_fall_out: 0.0105 - val_mcc: 0.8315
Epoch 25/100
63/63 [==============================] - 7s 109ms/step - loss: 0.4769 - accuracy: 0.8413 - recall: 0.8044 - precision: 0.8847 - AUROC: 0.9846 - AUPRC: 0.9191 - f1_score: 0.8426 - balanced_accuracy: 0.8964 - specificity: 0.9884 - miss_rate: 0.1956 - fall_out: 0.0116 - mcc: 0.8272 - val_loss: 0.4500 - val_accuracy: 0.8528 - val_recall: 0.8232 - val_precision: 0.8949 - val_AUROC: 0.9862 - val_AUPRC: 0.9281 - val_f1_score: 0.8576 - val_balanced_accuracy: 0.9062 - val_specificity: 0.9893 - val_miss_rate: 0.1768 - val_fall_out: 0.0107 - val_mcc: 0.8434
Epoch 26/100
63/63 [==============================] - 7s 108ms/step - loss: 0.4511 - accuracy: 0.8477 - recall: 0.8124 - precision: 0.8849 - AUROC: 0.9868 - AUPRC: 0.9256 - f1_score: 0.8471 - balanced_accuracy: 0.9003 - specificity: 0.9883 - miss_rate: 0.1876 - fall_out: 0.0117 - mcc: 0.8318 - val_loss: 0.4234 - val_accuracy: 0.8623 - val_recall: 0.8297 - val_precision: 0.9005 - val_AUROC: 0.9874 - val_AUPRC: 0.9353 - val_f1_score: 0.8637 - val_balanced_accuracy: 0.9098 - val_specificity: 0.9898 - val_miss_rate: 0.1703 - val_fall_out: 0.0102 - val_mcc: 0.8501
Epoch 27/100
63/63 [==============================] - 7s 108ms/step - loss: 0.4499 - accuracy: 0.8491 - recall: 0.8110 - precision: 0.8905 - AUROC: 0.9864 - AUPRC: 0.9249 - f1_score: 0.8489 - balanced_accuracy: 0.9000 - specificity: 0.9889 - miss_rate: 0.1890 - fall_out: 0.0111 - mcc: 0.8341 - val_loss: 0.4225 - val_accuracy: 0.8698 - val_recall: 0.8373 - val_precision: 0.8984 - val_AUROC: 0.9873 - val_AUPRC: 0.9367 - val_f1_score: 0.8668 - val_balanced_accuracy: 0.9134 - val_specificity: 0.9895 - val_miss_rate: 0.1627 - val_fall_out: 0.0105 - val_mcc: 0.8532
Epoch 28/100
63/63 [==============================] - 7s 109ms/step - loss: 0.4140 - accuracy: 0.8647 - recall: 0.8320 - precision: 0.9003 - AUROC: 0.9881 - AUPRC: 0.9357 - f1_score: 0.8648 - balanced_accuracy: 0.9109 - specificity: 0.9898 - miss_rate: 0.1680 - fall_out: 0.0102 - mcc: 0.8512 - val_loss: 0.4139 - val_accuracy: 0.8698 - val_recall: 0.8473 - val_precision: 0.9000 - val_AUROC: 0.9870 - val_AUPRC: 0.9380 - val_f1_score: 0.8728 - val_balanced_accuracy: 0.9184 - val_specificity: 0.9895 - val_miss_rate: 0.1527 - val_fall_out: 0.0105 - val_mcc: 0.8597
Epoch 29/100
63/63 [==============================] - 7s 108ms/step - loss: 0.4196 - accuracy: 0.8632 - recall: 0.8302 - precision: 0.8974 - AUROC: 0.9880 - AUPRC: 0.9343 - f1_score: 0.8625 - balanced_accuracy: 0.9098 - specificity: 0.9895 - miss_rate: 0.1698 - fall_out: 0.0105 - mcc: 0.8486 - val_loss: 0.3917 - val_accuracy: 0.8738 - val_recall: 0.8503 - val_precision: 0.8998 - val_AUROC: 0.9886 - val_AUPRC: 0.9421 - val_f1_score: 0.8744 - val_balanced_accuracy: 0.9199 - val_specificity: 0.9895 - val_miss_rate: 0.1497 - val_fall_out: 0.0105 - val_mcc: 0.8613
Epoch 30/100
63/63 [==============================] - 7s 108ms/step - loss: 0.3868 - accuracy: 0.8716 - recall: 0.8433 - precision: 0.9016 - AUROC: 0.9895 - AUPRC: 0.9429 - f1_score: 0.8715 - balanced_accuracy: 0.9165 - specificity: 0.9898 - miss_rate: 0.1567 - fall_out: 0.0102 - mcc: 0.8583 - val_loss: 0.4368 - val_accuracy: 0.8563 - val_recall: 0.8343 - val_precision: 0.8862 - val_AUROC: 0.9867 - val_AUPRC: 0.9339 - val_f1_score: 0.8594 - val_balanced_accuracy: 0.9112 - val_specificity: 0.9881 - val_miss_rate: 0.1657 - val_fall_out: 0.0119 - val_mcc: 0.8448
Epoch 31/100
63/63 [==============================] - 7s 108ms/step - loss: 0.3847 - accuracy: 0.8741 - recall: 0.8479 - precision: 0.9031 - AUROC: 0.9893 - AUPRC: 0.9417 - f1_score: 0.8747 - balanced_accuracy: 0.9189 - specificity: 0.9899 - miss_rate: 0.1521 - fall_out: 0.0101 - mcc: 0.8618 - val_loss: 0.3894 - val_accuracy: 0.8773 - val_recall: 0.8483 - val_precision: 0.9059 - val_AUROC: 0.9890 - val_AUPRC: 0.9435 - val_f1_score: 0.8761 - val_balanced_accuracy: 0.9192 - val_specificity: 0.9902 - val_miss_rate: 0.1517 - val_fall_out: 0.0098 - val_mcc: 0.8634
Epoch 32/100
63/63 [==============================] - 7s 109ms/step - loss: 0.3549 - accuracy: 0.8824 - recall: 0.8570 - precision: 0.9101 - AUROC: 0.9907 - AUPRC: 0.9483 - f1_score: 0.8827 - balanced_accuracy: 0.9238 - specificity: 0.9906 - miss_rate: 0.1430 - fall_out: 0.0094 - mcc: 0.8706 - val_loss: 0.3742 - val_accuracy: 0.8863 - val_recall: 0.8653 - val_precision: 0.9076 - val_AUROC: 0.9890 - val_AUPRC: 0.9460 - val_f1_score: 0.8859 - val_balanced_accuracy: 0.9278 - val_specificity: 0.9902 - val_miss_rate: 0.1347 - val_fall_out: 0.0098 - val_mcc: 0.8739
Epoch 33/100
63/63 [==============================] - 7s 108ms/step - loss: 0.3519 - accuracy: 0.8878 - recall: 0.8636 - precision: 0.9111 - AUROC: 0.9909 - AUPRC: 0.9502 - f1_score: 0.8867 - balanced_accuracy: 0.9271 - specificity: 0.9906 - miss_rate: 0.1364 - fall_out: 0.0094 - mcc: 0.8749 - val_loss: 0.3775 - val_accuracy: 0.8768 - val_recall: 0.8538 - val_precision: 0.8969 - val_AUROC: 0.9887 - val_AUPRC: 0.9481 - val_f1_score: 0.8748 - val_balanced_accuracy: 0.9214 - val_specificity: 0.9891 - val_miss_rate: 0.1462 - val_fall_out: 0.0109 - val_mcc: 0.8616
Epoch 34/100
63/63 [==============================] - 7s 108ms/step - loss: 0.3311 - accuracy: 0.8912 - recall: 0.8666 - precision: 0.9136 - AUROC: 0.9920 - AUPRC: 0.9545 - f1_score: 0.8895 - balanced_accuracy: 0.9288 - specificity: 0.9909 - miss_rate: 0.1334 - fall_out: 0.0091 - mcc: 0.8780 - val_loss: 0.3840 - val_accuracy: 0.8818 - val_recall: 0.8613 - val_precision: 0.9096 - val_AUROC: 0.9880 - val_AUPRC: 0.9458 - val_f1_score: 0.8848 - val_balanced_accuracy: 0.9259 - val_specificity: 0.9905 - val_miss_rate: 0.1387 - val_fall_out: 0.0095 - val_mcc: 0.8728
63/63 [==============================] - 4s 65ms/step - loss: 0.1291 - accuracy: 0.9684 - recall: 0.9543 - precision: 0.9791 - AUROC: 0.9993 - AUPRC: 0.9947 - f1_score: 0.9665 - balanced_accuracy: 0.9760 - specificity: 0.9977 - miss_rate: 0.0457 - fall_out: 0.0023 - mcc: 0.9629
16/16 [==============================] - 3s 194ms/step - loss: 0.3840 - accuracy: 0.8818 - recall: 0.8613 - precision: 0.9096 - AUROC: 0.9880 - AUPRC: 0.9458 - f1_score: 0.8848 - balanced_accuracy: 0.9259 - specificity: 0.9905 - miss_rate: 0.1387 - fall_out: 0.0095 - mcc: 0.8728
-- HOLDOUT 10 --
-- 6 Uncorrelated features: [Pearson+Spearman]
['mfcc10_var', 'mfcc17_mean', 'mfcc11_var', 'mfcc16_mean', 'tempo', 'mfcc19_mean']
-- Actually uncorrelated features: [confirmed via MIC]
[]
-- 1 Correlated features: [Pearson]
['spectral_centroid_mean']
- Removing uncorrelated/correlated features: ['spectral_centroid_mean']
- Building Fixed MLP:
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
features_data (InputLayer) [(None, 56)] 0
dense (Dense) (None, 256) 14592
dropout (Dropout) (None, 256) 0
dense_1 (Dense) (None, 256) 65792
dropout_1 (Dropout) (None, 256) 0
dense_2 (Dense) (None, 128) 32896
dropout_2 (Dropout) (None, 128) 0
dense_3 (Dense) (None, 128) 16512
dropout_3 (Dropout) (None, 128) 0
dense_4 (Dense) (None, 10) 1290
=================================================================
Total params: 131,082
Trainable params: 131,082
Non-trainable params: 0
_________________________________________________________________
- Building Fixed CNN:
Model: "model_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
mfccs_data (InputLayer) [(None, 20, 130, 1)] 0
conv2d (Conv2D) (None, 20, 130, 256) 2560
max_pooling2d (MaxPooling2D (None, 10, 65, 256) 0
)
dropout_4 (Dropout) (None, 10, 65, 256) 0
conv2d_1 (Conv2D) (None, 10, 65, 256) 590080
max_pooling2d_1 (MaxPooling (None, 5, 33, 256) 0
2D)
dropout_5 (Dropout) (None, 5, 33, 256) 0
conv2d_2 (Conv2D) (None, 5, 33, 256) 590080
max_pooling2d_2 (MaxPooling (None, 3, 17, 256) 0
2D)
dropout_6 (Dropout) (None, 3, 17, 256) 0
conv2d_3 (Conv2D) (None, 3, 17, 512) 2097664
max_pooling2d_3 (MaxPooling (None, 2, 9, 512) 0
2D)
flatten (Flatten) (None, 9216) 0
dense_5 (Dense) (None, 128) 1179776
dropout_7 (Dropout) (None, 128) 0
dense_6 (Dense) (None, 128) 16512
dropout_8 (Dropout) (None, 128) 0
dense_7 (Dense) (None, 10) 1290
=================================================================
Total params: 4,477,962
Trainable params: 4,477,962
Non-trainable params: 0
_________________________________________________________________
- Building MMNN:
Model: "model_2"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
mfccs_data (InputLayer) [(None, 20, 130, 1) 0 []
]
conv2d (Conv2D) (None, 20, 130, 256 2560 ['mfccs_data[0][0]']
)
max_pooling2d (MaxPooling2D) (None, 10, 65, 256) 0 ['conv2d[0][0]']
dropout_4 (Dropout) (None, 10, 65, 256) 0 ['max_pooling2d[0][0]']
conv2d_1 (Conv2D) (None, 10, 65, 256) 590080 ['dropout_4[0][0]']
max_pooling2d_1 (MaxPooling2D) (None, 5, 33, 256) 0 ['conv2d_1[0][0]']
dropout_5 (Dropout) (None, 5, 33, 256) 0 ['max_pooling2d_1[0][0]']
conv2d_2 (Conv2D) (None, 5, 33, 256) 590080 ['dropout_5[0][0]']
features_data (InputLayer) [(None, 56)] 0 []
max_pooling2d_2 (MaxPooling2D) (None, 3, 17, 256) 0 ['conv2d_2[0][0]']
dense (Dense) (None, 256) 14592 ['features_data[0][0]']
dropout_6 (Dropout) (None, 3, 17, 256) 0 ['max_pooling2d_2[0][0]']
dropout (Dropout) (None, 256) 0 ['dense[0][0]']
conv2d_3 (Conv2D) (None, 3, 17, 512) 2097664 ['dropout_6[0][0]']
dense_1 (Dense) (None, 256) 65792 ['dropout[0][0]']
max_pooling2d_3 (MaxPooling2D) (None, 2, 9, 512) 0 ['conv2d_3[0][0]']
dropout_1 (Dropout) (None, 256) 0 ['dense_1[0][0]']
flatten (Flatten) (None, 9216) 0 ['max_pooling2d_3[0][0]']
dense_2 (Dense) (None, 128) 32896 ['dropout_1[0][0]']
dense_5 (Dense) (None, 128) 1179776 ['flatten[0][0]']
dropout_2 (Dropout) (None, 128) 0 ['dense_2[0][0]']
dropout_7 (Dropout) (None, 128) 0 ['dense_5[0][0]']
dense_3 (Dense) (None, 128) 16512 ['dropout_2[0][0]']
dense_6 (Dense) (None, 128) 16512 ['dropout_7[0][0]']
dropout_3 (Dropout) (None, 128) 0 ['dense_3[0][0]']
dropout_8 (Dropout) (None, 128) 0 ['dense_6[0][0]']
concatenate (Concatenate) (None, 256) 0 ['dropout_3[0][0]',
'dropout_8[0][0]']
dense_8 (Dense) (None, 64) 16448 ['concatenate[0][0]']
dense_9 (Dense) (None, 10) 650 ['dense_8[0][0]']
==================================================================================================
Total params: 4,623,562
Trainable params: 4,623,562
Non-trainable params: 0
__________________________________________________________________________________________________
- Training MMNN model:
Epoch 1/100
63/63 [==============================] - 9s 114ms/step - loss: 2.1690 - accuracy: 0.2117 - recall: 0.0267 - precision: 0.4942 - AUROC: 0.6676 - AUPRC: 0.2018 - f1_score: 0.0506 - balanced_accuracy: 0.5118 - specificity: 0.9970 - miss_rate: 0.9733 - fall_out: 0.0030 - mcc: 0.0968 - val_loss: 1.7434 - val_accuracy: 0.3660 - val_recall: 0.1082 - val_precision: 0.5886 - val_AUROC: 0.8227 - val_AUPRC: 0.3855 - val_f1_score: 0.1827 - val_balanced_accuracy: 0.5499 - val_specificity: 0.9916 - val_miss_rate: 0.8918 - val_fall_out: 0.0084 - val_mcc: 0.2228
Epoch 2/100
63/63 [==============================] - 7s 110ms/step - loss: 1.7013 - accuracy: 0.3758 - recall: 0.1410 - precision: 0.6100 - AUROC: 0.8312 - AUPRC: 0.3870 - f1_score: 0.2291 - balanced_accuracy: 0.5655 - specificity: 0.9900 - miss_rate: 0.8590 - fall_out: 0.0100 - mcc: 0.2615 - val_loss: 1.4300 - val_accuracy: 0.4677 - val_recall: 0.2494 - val_precision: 0.7044 - val_AUROC: 0.8906 - val_AUPRC: 0.5316 - val_f1_score: 0.3683 - val_balanced_accuracy: 0.6189 - val_specificity: 0.9884 - val_miss_rate: 0.7506 - val_fall_out: 0.0116 - val_mcc: 0.3860
Epoch 3/100
63/63 [==============================] - 7s 110ms/step - loss: 1.4744 - accuracy: 0.4602 - recall: 0.2316 - precision: 0.6463 - AUROC: 0.8774 - AUPRC: 0.4829 - f1_score: 0.3410 - balanced_accuracy: 0.6088 - specificity: 0.9859 - miss_rate: 0.7684 - fall_out: 0.0141 - mcc: 0.3510 - val_loss: 1.2705 - val_accuracy: 0.5418 - val_recall: 0.2909 - val_precision: 0.7959 - val_AUROC: 0.9140 - val_AUPRC: 0.6158 - val_f1_score: 0.4261 - val_balanced_accuracy: 0.6413 - val_specificity: 0.9917 - val_miss_rate: 0.7091 - val_fall_out: 0.0083 - val_mcc: 0.4518
Epoch 4/100
63/63 [==============================] - 8s 110ms/step - loss: 1.3465 - accuracy: 0.5085 - recall: 0.2893 - precision: 0.6896 - AUROC: 0.8986 - AUPRC: 0.5547 - f1_score: 0.4076 - balanced_accuracy: 0.6374 - specificity: 0.9855 - miss_rate: 0.7107 - fall_out: 0.0145 - mcc: 0.4113 - val_loss: 1.1658 - val_accuracy: 0.5864 - val_recall: 0.3345 - val_precision: 0.7943 - val_AUROC: 0.9293 - val_AUPRC: 0.6558 - val_f1_score: 0.4708 - val_balanced_accuracy: 0.6624 - val_specificity: 0.9904 - val_miss_rate: 0.6655 - val_fall_out: 0.0096 - val_mcc: 0.4853
Epoch 5/100
63/63 [==============================] - 7s 110ms/step - loss: 1.2080 - accuracy: 0.5645 - recall: 0.3685 - precision: 0.7262 - AUROC: 0.9179 - AUPRC: 0.6214 - f1_score: 0.4889 - balanced_accuracy: 0.6765 - specificity: 0.9846 - miss_rate: 0.6315 - fall_out: 0.0154 - mcc: 0.4826 - val_loss: 0.9978 - val_accuracy: 0.6640 - val_recall: 0.4612 - val_precision: 0.8209 - val_AUROC: 0.9465 - val_AUPRC: 0.7359 - val_f1_score: 0.5906 - val_balanced_accuracy: 0.7250 - val_specificity: 0.9888 - val_miss_rate: 0.5388 - val_fall_out: 0.0112 - val_mcc: 0.5863
Epoch 6/100
63/63 [==============================] - 7s 110ms/step - loss: 1.1092 - accuracy: 0.6045 - recall: 0.4364 - precision: 0.7497 - AUROC: 0.9307 - AUPRC: 0.6697 - f1_score: 0.5517 - balanced_accuracy: 0.7101 - specificity: 0.9838 - miss_rate: 0.5636 - fall_out: 0.0162 - mcc: 0.5384 - val_loss: 0.9174 - val_accuracy: 0.6790 - val_recall: 0.5233 - val_precision: 0.8107 - val_AUROC: 0.9537 - val_AUPRC: 0.7655 - val_f1_score: 0.6360 - val_balanced_accuracy: 0.7549 - val_specificity: 0.9864 - val_miss_rate: 0.4767 - val_fall_out: 0.0136 - val_mcc: 0.6223
Epoch 7/100
63/63 [==============================] - 7s 109ms/step - loss: 1.0224 - accuracy: 0.6437 - recall: 0.4894 - precision: 0.7610 - AUROC: 0.9409 - AUPRC: 0.7097 - f1_score: 0.5957 - balanced_accuracy: 0.7361 - specificity: 0.9829 - miss_rate: 0.5106 - fall_out: 0.0171 - mcc: 0.5776 - val_loss: 0.8504 - val_accuracy: 0.7156 - val_recall: 0.5699 - val_precision: 0.8282 - val_AUROC: 0.9604 - val_AUPRC: 0.7948 - val_f1_score: 0.6752 - val_balanced_accuracy: 0.7784 - val_specificity: 0.9869 - val_miss_rate: 0.4301 - val_fall_out: 0.0131 - val_mcc: 0.6598
Epoch 8/100
63/63 [==============================] - 7s 110ms/step - loss: 0.9623 - accuracy: 0.6657 - recall: 0.5331 - precision: 0.7757 - AUROC: 0.9473 - AUPRC: 0.7397 - f1_score: 0.6319 - balanced_accuracy: 0.7580 - specificity: 0.9829 - miss_rate: 0.4669 - fall_out: 0.0171 - mcc: 0.6118 - val_loss: 0.7831 - val_accuracy: 0.7356 - val_recall: 0.6219 - val_precision: 0.8330 - val_AUROC: 0.9652 - val_AUPRC: 0.8216 - val_f1_score: 0.7122 - val_balanced_accuracy: 0.8040 - val_specificity: 0.9861 - val_miss_rate: 0.3781 - val_fall_out: 0.0139 - val_mcc: 0.6940
Epoch 9/100
63/63 [==============================] - 8s 110ms/step - loss: 0.9162 - accuracy: 0.6841 - recall: 0.5624 - precision: 0.7859 - AUROC: 0.9522 - AUPRC: 0.7566 - f1_score: 0.6556 - balanced_accuracy: 0.7727 - specificity: 0.9830 - miss_rate: 0.4376 - fall_out: 0.0170 - mcc: 0.6347 - val_loss: 0.7398 - val_accuracy: 0.7581 - val_recall: 0.6314 - val_precision: 0.8619 - val_AUROC: 0.9699 - val_AUPRC: 0.8432 - val_f1_score: 0.7289 - val_balanced_accuracy: 0.8101 - val_specificity: 0.9888 - val_miss_rate: 0.3686 - val_fall_out: 0.0112 - val_mcc: 0.7141
Epoch 10/100
63/63 [==============================] - 7s 109ms/step - loss: 0.8461 - accuracy: 0.7147 - recall: 0.6090 - precision: 0.8076 - AUROC: 0.9584 - AUPRC: 0.7892 - f1_score: 0.6944 - balanced_accuracy: 0.7964 - specificity: 0.9839 - miss_rate: 0.3910 - fall_out: 0.0161 - mcc: 0.6736 - val_loss: 0.7218 - val_accuracy: 0.7511 - val_recall: 0.6550 - val_precision: 0.8263 - val_AUROC: 0.9704 - val_AUPRC: 0.8450 - val_f1_score: 0.7307 - val_balanced_accuracy: 0.8198 - val_specificity: 0.9847 - val_miss_rate: 0.3450 - val_fall_out: 0.0153 - val_mcc: 0.7103
Epoch 11/100
63/63 [==============================] - 7s 109ms/step - loss: 0.8025 - accuracy: 0.7270 - recall: 0.6211 - precision: 0.8078 - AUROC: 0.9623 - AUPRC: 0.8054 - f1_score: 0.7023 - balanced_accuracy: 0.8023 - specificity: 0.9836 - miss_rate: 0.3789 - fall_out: 0.0164 - mcc: 0.6809 - val_loss: 0.6941 - val_accuracy: 0.7672 - val_recall: 0.6650 - val_precision: 0.8618 - val_AUROC: 0.9724 - val_AUPRC: 0.8545 - val_f1_score: 0.7507 - val_balanced_accuracy: 0.8266 - val_specificity: 0.9881 - val_miss_rate: 0.3350 - val_fall_out: 0.0119 - val_mcc: 0.7343
Epoch 12/100
63/63 [==============================] - 7s 109ms/step - loss: 0.7444 - accuracy: 0.7484 - recall: 0.6571 - precision: 0.8259 - AUROC: 0.9675 - AUPRC: 0.8291 - f1_score: 0.7319 - balanced_accuracy: 0.8208 - specificity: 0.9846 - miss_rate: 0.3429 - fall_out: 0.0154 - mcc: 0.7114 - val_loss: 0.5985 - val_accuracy: 0.8037 - val_recall: 0.7176 - val_precision: 0.8813 - val_AUROC: 0.9790 - val_AUPRC: 0.8915 - val_f1_score: 0.7911 - val_balanced_accuracy: 0.8534 - val_specificity: 0.9893 - val_miss_rate: 0.2824 - val_fall_out: 0.0107 - val_mcc: 0.7754
Epoch 13/100
63/63 [==============================] - 7s 110ms/step - loss: 0.7071 - accuracy: 0.7611 - recall: 0.6849 - precision: 0.8351 - AUROC: 0.9703 - AUPRC: 0.8415 - f1_score: 0.7525 - balanced_accuracy: 0.8349 - specificity: 0.9850 - miss_rate: 0.3151 - fall_out: 0.0150 - mcc: 0.7324 - val_loss: 0.5891 - val_accuracy: 0.8077 - val_recall: 0.7231 - val_precision: 0.8736 - val_AUROC: 0.9798 - val_AUPRC: 0.8907 - val_f1_score: 0.7912 - val_balanced_accuracy: 0.8557 - val_specificity: 0.9884 - val_miss_rate: 0.2769 - val_fall_out: 0.0116 - val_mcc: 0.7746
Epoch 14/100
63/63 [==============================] - 7s 110ms/step - loss: 0.6572 - accuracy: 0.7759 - recall: 0.7045 - precision: 0.8452 - AUROC: 0.9744 - AUPRC: 0.8604 - f1_score: 0.7685 - balanced_accuracy: 0.8451 - specificity: 0.9857 - miss_rate: 0.2955 - fall_out: 0.0143 - mcc: 0.7491 - val_loss: 0.5596 - val_accuracy: 0.8117 - val_recall: 0.7586 - val_precision: 0.8844 - val_AUROC: 0.9806 - val_AUPRC: 0.9004 - val_f1_score: 0.8167 - val_balanced_accuracy: 0.8738 - val_specificity: 0.9890 - val_miss_rate: 0.2414 - val_fall_out: 0.0110 - val_mcc: 0.8009
Epoch 15/100
63/63 [==============================] - 8s 113ms/step - loss: 0.6035 - accuracy: 0.7996 - recall: 0.7370 - precision: 0.8565 - AUROC: 0.9773 - AUPRC: 0.8777 - f1_score: 0.7922 - balanced_accuracy: 0.8616 - specificity: 0.9863 - miss_rate: 0.2630 - fall_out: 0.0137 - mcc: 0.7737 - val_loss: 0.5328 - val_accuracy: 0.8297 - val_recall: 0.7762 - val_precision: 0.8832 - val_AUROC: 0.9824 - val_AUPRC: 0.9070 - val_f1_score: 0.8262 - val_balanced_accuracy: 0.8824 - val_specificity: 0.9886 - val_miss_rate: 0.2238 - val_fall_out: 0.0114 - val_mcc: 0.8103
Epoch 16/100
63/63 [==============================] - 7s 109ms/step - loss: 0.6061 - accuracy: 0.7955 - recall: 0.7338 - precision: 0.8535 - AUROC: 0.9774 - AUPRC: 0.8782 - f1_score: 0.7891 - balanced_accuracy: 0.8599 - specificity: 0.9860 - miss_rate: 0.2662 - fall_out: 0.0140 - mcc: 0.7703 - val_loss: 0.4874 - val_accuracy: 0.8373 - val_recall: 0.7867 - val_precision: 0.8911 - val_AUROC: 0.9850 - val_AUPRC: 0.9201 - val_f1_score: 0.8356 - val_balanced_accuracy: 0.8880 - val_specificity: 0.9893 - val_miss_rate: 0.2133 - val_fall_out: 0.0107 - val_mcc: 0.8206
Epoch 17/100
63/63 [==============================] - 7s 110ms/step - loss: 0.5355 - accuracy: 0.8229 - recall: 0.7735 - precision: 0.8733 - AUROC: 0.9818 - AUPRC: 0.9013 - f1_score: 0.8204 - balanced_accuracy: 0.8805 - specificity: 0.9875 - miss_rate: 0.2265 - fall_out: 0.0125 - mcc: 0.8036 - val_loss: 0.4920 - val_accuracy: 0.8408 - val_recall: 0.7972 - val_precision: 0.8874 - val_AUROC: 0.9848 - val_AUPRC: 0.9197 - val_f1_score: 0.8399 - val_balanced_accuracy: 0.8930 - val_specificity: 0.9888 - val_miss_rate: 0.2028 - val_fall_out: 0.0112 - val_mcc: 0.8246
Epoch 18/100
63/63 [==============================] - 7s 110ms/step - loss: 0.5038 - accuracy: 0.8325 - recall: 0.7858 - precision: 0.8744 - AUROC: 0.9838 - AUPRC: 0.9089 - f1_score: 0.8278 - balanced_accuracy: 0.8866 - specificity: 0.9875 - miss_rate: 0.2142 - fall_out: 0.0125 - mcc: 0.8112 - val_loss: 0.4883 - val_accuracy: 0.8378 - val_recall: 0.7877 - val_precision: 0.8837 - val_AUROC: 0.9854 - val_AUPRC: 0.9190 - val_f1_score: 0.8329 - val_balanced_accuracy: 0.8881 - val_specificity: 0.9885 - val_miss_rate: 0.2123 - val_fall_out: 0.0115 - val_mcc: 0.8172
63/63 [==============================] - 4s 66ms/step - loss: 0.2998 - accuracy: 0.9057 - recall: 0.8687 - precision: 0.9430 - AUROC: 0.9953 - AUPRC: 0.9688 - f1_score: 0.9044 - balanced_accuracy: 0.9315 - specificity: 0.9942 - miss_rate: 0.1313 - fall_out: 0.0058 - mcc: 0.8951
16/16 [==============================] - 3s 196ms/step - loss: 0.4883 - accuracy: 0.8378 - recall: 0.7877 - precision: 0.8837 - AUROC: 0.9854 - AUPRC: 0.9190 - f1_score: 0.8329 - balanced_accuracy: 0.8881 - specificity: 0.9885 - miss_rate: 0.2123 - fall_out: 0.0115 - mcc: 0.8172
MMNN_metrics_estimate = model_metrics_holdout_estimate(MMNN_metrics, number_of_splits)
print(f"MMNN Metrics - {number_of_splits}-holdouts estimate:")
print(f"Accuracy : train - {MMNN_metrics_estimate['accuracy_train']} -- test - {MMNN_metrics_estimate['accuracy_test']}")
print(f"AUROC : train - {MMNN_metrics_estimate['AUROC_train']} -- test - {MMNN_metrics_estimate['AUROC_test']}")
print(f"AUPRC : train - {MMNN_metrics_estimate['AUPRC_train']} -- test - {MMNN_metrics_estimate['AUPRC_test']}")
print("-"*80)
print("MMNN - Train history:")
plot_train_history(MMNN_history)
MMNN Metrics - 10-holdouts estimate: Accuracy : train - 0.9423722505569458 -- test - 0.8631447136402131 AUROC : train - 0.9974672317504882 -- test - 0.9861195266246796 AUPRC : train - 0.9831790268421173 -- test - 0.9317175269126892 -------------------------------------------------------------------------------- MMNN - Train history:
Hereby all the results from all the different architectures designed are grouped and visualized via barplots.
The following cells format the results for the barplot visualization.
results = {"model":[], "accuracy":[], "AUROC":[], "AUPRC":[]}
# MLP
for metrics, model in ((MLP_fixed_metrics, "Fixed MLP"), (MLP_tuned_metrics, "Tuned MLP"), (MLP_other_features_metrics, "No Mfccs MLP")):
for window in ("window_30s", "window_3s"):
for holdout in (metrics[window]):
results["model"].append(model + "_" + window)
results["accuracy"].append(holdout["test_evaluation"]["accuracy"])
results["AUROC"].append(holdout["test_evaluation"]["AUROC"])
results["AUPRC"].append(holdout["test_evaluation"]["AUPRC"])
# CNN and MMNN
for metrics, model in ((CNN_MelS_30s_metrics, "30s Mel spectrogram CNN"), (CNN_S_3s_metrics, "3s Spectrogram CNN"), (CNN_MelS_3s_metrics, "3s Mel spectrogram CNN"), (CNN_mfccs_3s_metrics, "3s Mfccs CNN"), (MMNN_metrics, "MMNN")):
for holdout in metrics:
results["model"].append(model)
results["accuracy"].append(holdout["test_evaluation"]["accuracy"])
results["AUROC"].append(holdout["test_evaluation"]["AUROC"])
results["AUPRC"].append(holdout["test_evaluation"]["AUPRC"])
resultsDF = pd.DataFrame(results)
resultsDF
| model | accuracy | AUROC | AUPRC | |
|---|---|---|---|---|
| 0 | Fixed MLP_window_30s | 0.675000 | 0.939958 | 0.716022 |
| 1 | Fixed MLP_window_30s | 0.655000 | 0.942044 | 0.728116 |
| 2 | Fixed MLP_window_30s | 0.630000 | 0.917342 | 0.655936 |
| 3 | Fixed MLP_window_30s | 0.715000 | 0.959424 | 0.803921 |
| 4 | Fixed MLP_window_30s | 0.630000 | 0.937732 | 0.706818 |
| ... | ... | ... | ... | ... |
| 105 | MMNN | 0.875313 | 0.986416 | 0.937027 |
| 106 | MMNN | 0.869805 | 0.986065 | 0.936040 |
| 107 | MMNN | 0.791688 | 0.977679 | 0.877112 |
| 108 | MMNN | 0.881823 | 0.988001 | 0.945829 |
| 109 | MMNN | 0.837757 | 0.985351 | 0.918975 |
110 rows × 4 columns
from barplots import barplots
barplots(
resultsDF,
groupby=["model"],
orientation="horizontal",
height=12,
colors={
'Fixed MLP_window_30s':'blue',
'Fixed MLP_window_3s':'orange',
'Tuned MLP_window_30s':'green',
'Tuned MLP_window_3s':'red',
'No Mfccs MLP window 30s':'purple',
'No Mfccs MLP window 3s':'brown',
'30s Mel spectrogram CNN':'pink',
'3s Spectrogram CNN':'gray',
'3s Mel spectrogram CNN':'olive',
'3s Mfccs CNN':'cyan',
'MMNN':'black'}
)
((<Figure size 2400x660 with 1 Axes>,
<Figure size 2400x660 with 1 Axes>,
<Figure size 2400x660 with 1 Axes>),
(array([<AxesSubplot:title={'center':'AUROC'}, xlabel='AUROC'>],
dtype=object),
array([<AxesSubplot:title={'center':'Accuracy'}, xlabel='Accuracy'>],
dtype=object),
array([<AxesSubplot:title={'center':'AUPRC'}, xlabel='AUPRC'>],
dtype=object)))